diff --git "a/demo/ort-phi3/dist/esm/ort.wasm-core.min.js.map" "b/demo/ort-phi3/dist/esm/ort.wasm-core.min.js.map" new file mode 100644--- /dev/null +++ "b/demo/ort-phi3/dist/esm/ort.wasm-core.min.js.map" @@ -0,0 +1,7 @@ +{ + "version": 3, + "sources": ["../../../common/lib/backend-impl.ts", "../../../common/lib/backend.ts", "../../../common/lib/version.ts", "../../../common/lib/env-impl.ts", "../../../common/lib/env.ts", "../../../common/lib/tensor-conversion-impl.ts", "../../../common/lib/tensor-factory-impl.ts", "../../../common/lib/tensor-impl-type-mapping.ts", "../../../common/lib/tensor-utils-impl.ts", "../../../common/lib/tensor-impl.ts", "../../../common/lib/tensor.ts", "../../../common/lib/trace.ts", "../../../common/lib/inference-session-impl.ts", "../../../common/lib/inference-session.ts", "../../../common/lib/tensor-conversion.ts", "../../../common/lib/tensor-factory.ts", "../../../common/lib/onnx-model.ts", "../../../common/lib/onnx-value.ts", "../../../common/lib/training-session-impl.ts", "../../../common/lib/training-session.ts", "../../../common/lib/index.ts", "nodejs-ignore:fs", "nodejs-ignore:path", "../../lib/wasm/binding/ort-wasm.js", "../../lib/wasm/wasm-factory.ts", "../../lib/wasm/wasm-utils.ts", "../../lib/wasm/run-options.ts", "../../lib/wasm/session-options.ts", "../../lib/wasm/wasm-common.ts", "../../lib/wasm/wasm-utils-load-file.ts", "../../lib/wasm/wasm-core-impl.ts", "../../lib/wasm/proxy-wrapper.ts", "../../lib/wasm/session-handler-inference.ts", "../../lib/backend-wasm.ts", "../../lib/backend-wasm-inference.ts", "../../lib/index.ts", "../../lib/version.ts"], + "sourcesContent": ["// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Backend} from './backend.js';\nimport {InferenceSession} from './inference-session.js';\n\ninterface BackendInfo {\n backend: Backend;\n priority: number;\n\n initPromise?: Promise;\n initialized?: boolean;\n aborted?: boolean;\n error?: string;\n}\n\nconst backends: Map = new Map();\nconst backendsSortedByPriority: string[] = [];\n\n/**\n * Register a backend.\n *\n * @param name - the name as a key to lookup as an execution provider.\n * @param backend - the backend object.\n * @param priority - an integer indicating the priority of the backend. Higher number means higher priority. if priority\n * < 0, it will be considered as a 'beta' version and will not be used as a fallback backend by default.\n *\n * @ignore\n */\nexport const registerBackend = (name: string, backend: Backend, priority: number): void => {\n if (backend && typeof backend.init === 'function' && typeof backend.createInferenceSessionHandler === 'function') {\n const currentBackend = backends.get(name);\n if (currentBackend === undefined) {\n backends.set(name, {backend, priority});\n } else if (currentBackend.priority > priority) {\n // same name is already registered with a higher priority. skip registeration.\n return;\n } else if (currentBackend.priority === priority) {\n if (currentBackend.backend !== backend) {\n throw new Error(`cannot register backend \"${name}\" using priority ${priority}`);\n }\n }\n\n if (priority >= 0) {\n const i = backendsSortedByPriority.indexOf(name);\n if (i !== -1) {\n backendsSortedByPriority.splice(i, 1);\n }\n\n for (let i = 0; i < backendsSortedByPriority.length; i++) {\n if (backends.get(backendsSortedByPriority[i])!.priority <= priority) {\n backendsSortedByPriority.splice(i, 0, name);\n return;\n }\n }\n backendsSortedByPriority.push(name);\n }\n return;\n }\n\n throw new TypeError('not a valid backend');\n};\n\n/**\n * Try to resolve and initialize a backend.\n *\n * @param backendName - the name of the backend.\n * @returns the backend instance if resolved and initialized successfully, or an error message if failed.\n */\nconst tryResolveAndInitializeBackend = async(backendName: string): Promise => {\n const backendInfo = backends.get(backendName);\n if (!backendInfo) {\n return 'backend not found.';\n }\n\n if (backendInfo.initialized) {\n return backendInfo.backend;\n } else if (backendInfo.aborted) {\n return backendInfo.error!;\n } else {\n const isInitializing = !!backendInfo.initPromise;\n try {\n if (!isInitializing) {\n backendInfo.initPromise = backendInfo.backend.init(backendName);\n }\n await backendInfo.initPromise;\n backendInfo.initialized = true;\n return backendInfo.backend;\n } catch (e) {\n if (!isInitializing) {\n backendInfo.error = `${e}`;\n backendInfo.aborted = true;\n }\n return backendInfo.error!;\n } finally {\n delete backendInfo.initPromise;\n }\n }\n};\n\n/**\n * Resolve execution providers from the specific session options.\n *\n * @param options - the session options object.\n * @returns a promise that resolves to a tuple of an initialized backend instance and a session options object with\n * filtered EP list.\n *\n * @ignore\n */\nexport const resolveBackendAndExecutionProviders = async(options: InferenceSession.SessionOptions):\n Promise<[backend: Backend, options: InferenceSession.SessionOptions]> => {\n // extract backend hints from session options\n const eps = options.executionProviders || [];\n const backendHints = eps.map(i => typeof i === 'string' ? i : i.name);\n const backendNames = backendHints.length === 0 ? backendsSortedByPriority : backendHints;\n\n // try to resolve and initialize all requested backends\n let backend: Backend|undefined;\n const errors = [];\n const availableBackendNames = new Set();\n for (const backendName of backendNames) {\n const resolveResult = await tryResolveAndInitializeBackend(backendName);\n if (typeof resolveResult === 'string') {\n errors.push({name: backendName, err: resolveResult});\n } else {\n if (!backend) {\n backend = resolveResult;\n }\n if (backend === resolveResult) {\n availableBackendNames.add(backendName);\n }\n }\n }\n\n // if no backend is available, throw error.\n if (!backend) {\n throw new Error(`no available backend found. ERR: ${errors.map(e => `[${e.name}] ${e.err}`).join(', ')}`);\n }\n\n // for each explicitly requested backend, if it's not available, output warning message.\n for (const {name, err} of errors) {\n if (backendHints.includes(name)) {\n // eslint-disable-next-line no-console\n console.warn(`removing requested execution provider \"${\n name}\" from session options because it is not available: ${err}`);\n }\n }\n\n const filteredEps = eps.filter(i => availableBackendNames.has(typeof i === 'string' ? i : i.name));\n\n return [\n backend, new Proxy(options, {\n get: (target, prop) => {\n if (prop === 'executionProviders') {\n return filteredEps;\n }\n return Reflect.get(target, prop);\n }\n })\n ];\n };\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession} from './inference-session.js';\nimport {OnnxValue} from './onnx-value.js';\nimport {TrainingSession} from './training-session.js';\n\n/**\n * @ignore\n */\nexport declare namespace SessionHandler {\n type FeedsType = {[name: string]: OnnxValue};\n type FetchesType = {[name: string]: OnnxValue | null};\n type ReturnType = {[name: string]: OnnxValue};\n}\n\n/**\n * Represents shared SessionHandler functionality\n *\n * @ignore\n */\ninterface SessionHandler {\n dispose(): Promise;\n\n readonly inputNames: readonly string[];\n readonly outputNames: readonly string[];\n}\n\n/**\n * Represent a handler instance of an inference session.\n *\n * @ignore\n */\nexport interface InferenceSessionHandler extends SessionHandler {\n startProfiling(): void;\n endProfiling(): void;\n\n run(feeds: SessionHandler.FeedsType, fetches: SessionHandler.FetchesType,\n options: InferenceSession.RunOptions): Promise;\n}\n\n/**\n * Represent a handler instance of a training inference session.\n *\n * @ignore\n */\nexport interface TrainingSessionHandler extends SessionHandler {\n readonly evalInputNames: readonly string[];\n readonly evalOutputNames: readonly string[];\n\n lazyResetGrad(): Promise;\n runTrainStep(\n feeds: SessionHandler.FeedsType, fetches: SessionHandler.FetchesType,\n options: InferenceSession.RunOptions): Promise;\n runOptimizerStep(options: InferenceSession.RunOptions): Promise;\n runEvalStep(\n feeds: SessionHandler.FeedsType, fetches: SessionHandler.FetchesType,\n options: InferenceSession.RunOptions): Promise;\n\n getParametersSize(trainableOnly: boolean): Promise;\n loadParametersBuffer(buffer: Uint8Array, trainableOnly: boolean): Promise;\n getContiguousParameters(trainableOnly: boolean): Promise;\n}\n\n/**\n * Represent a backend that provides implementation of model inferencing.\n *\n * @ignore\n */\nexport interface Backend {\n /**\n * Initialize the backend asynchronously. Should throw when failed.\n */\n init(backendName: string): Promise;\n\n createInferenceSessionHandler(uriOrBuffer: string|Uint8Array, options?: InferenceSession.SessionOptions):\n Promise;\n\n createTrainingSessionHandler?\n (checkpointStateUriOrBuffer: TrainingSession.UriOrBuffer, trainModelUriOrBuffer: TrainingSession.UriOrBuffer,\n evalModelUriOrBuffer: TrainingSession.UriOrBuffer, optimizerModelUriOrBuffer: TrainingSession.UriOrBuffer,\n options: InferenceSession.SessionOptions): Promise;\n}\n\nexport {registerBackend} from './backend-impl.js';\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n// This file is generated by /js/scripts/update-version.ts\n// Do not modify file content manually.\n\nexport const version = '1.18.0';\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Env} from './env.js';\nimport {version} from './version.js';\n\ntype LogLevelType = Env['logLevel'];\n\nlet logLevelValue: Required = 'warning';\n\nexport const env: Env = {\n wasm: {} as Env.WebAssemblyFlags,\n webgl: {} as Env.WebGLFlags,\n webgpu: {} as Env.WebGpuFlags,\n versions: {common: version},\n\n set logLevel(value: LogLevelType) {\n if (value === undefined) {\n return;\n }\n if (typeof value !== 'string' || ['verbose', 'info', 'warning', 'error', 'fatal'].indexOf(value) === -1) {\n throw new Error(`Unsupported logging level: ${value}`);\n }\n logLevelValue = value;\n },\n get logLevel(): Required {\n return logLevelValue;\n },\n};\n\n// set property 'logLevel' so that they can be correctly transferred to worker by `postMessage()`.\nObject.defineProperty(env, 'logLevel', {enumerable: true});\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env as envImpl} from './env-impl.js';\n\nexport declare namespace Env {\n export type WasmPrefixOrFilePaths = string|{\n /* eslint-disable @typescript-eslint/naming-convention */\n 'ort-wasm.wasm'?: string;\n 'ort-wasm-threaded.wasm'?: string;\n 'ort-wasm-simd.wasm'?: string;\n 'ort-training-wasm-simd.wasm'?: string;\n 'ort-wasm-simd-threaded.wasm'?: string;\n /* eslint-enable @typescript-eslint/naming-convention */\n };\n export interface WebAssemblyFlags {\n /**\n * set or get number of thread(s). If omitted or set to 0, number of thread(s) will be determined by system. If set\n * to 1, no worker thread will be spawned.\n *\n * This setting is available only when WebAssembly multithread feature is available in current context.\n *\n * @defaultValue `0`\n */\n numThreads?: number;\n\n /**\n * set or get a boolean value indicating whether to enable SIMD. If set to false, SIMD will be forcely disabled.\n *\n * This setting is available only when WebAssembly SIMD feature is available in current context.\n *\n * @defaultValue `true`\n */\n simd?: boolean;\n\n /**\n * set or get a boolean value indicating whether to enable trace.\n *\n * @deprecated Use `env.trace` instead. If `env.trace` is set, this property will be ignored.\n * @defaultValue `false`\n */\n trace?: boolean;\n\n /**\n * Set or get a number specifying the timeout for initialization of WebAssembly backend, in milliseconds. A zero\n * value indicates no timeout is set.\n *\n * @defaultValue `0`\n */\n initTimeout?: number;\n\n /**\n * Set a custom URL prefix to the .wasm files or a set of overrides for each .wasm file. The override path should be\n * an absolute path.\n */\n wasmPaths?: WasmPrefixOrFilePaths;\n\n /**\n * Set or get a boolean value indicating whether to proxy the execution of main thread to a worker thread.\n *\n * @defaultValue `false`\n */\n proxy?: boolean;\n }\n\n export interface WebGLFlags {\n /**\n * Set or get the WebGL Context ID (webgl or webgl2).\n *\n * @defaultValue `'webgl2'`\n */\n contextId?: 'webgl'|'webgl2';\n /**\n * Get the WebGL rendering context.\n */\n readonly context: WebGLRenderingContext;\n /**\n * Set or get the maximum batch size for matmul. 0 means to disable batching.\n *\n * @deprecated\n */\n matmulMaxBatchSize?: number;\n /**\n * Set or get the texture cache mode.\n *\n * @defaultValue `'full'`\n */\n textureCacheMode?: 'initializerOnly'|'full';\n /**\n * Set or get the packed texture mode\n *\n * @defaultValue `false`\n */\n pack?: boolean;\n /**\n * Set or get whether enable async download.\n *\n * @defaultValue `false`\n */\n async?: boolean;\n }\n\n export interface WebGpuProfilingDataV1TensorMetadata {\n dims: readonly number[];\n dataType: string;\n }\n export interface WebGpuProfilingDataV1 {\n version: 1;\n inputsMetadata: readonly WebGpuProfilingDataV1TensorMetadata[];\n outputsMetadata: readonly WebGpuProfilingDataV1TensorMetadata[];\n kernelId: number;\n kernelType: string;\n kernelName: string;\n programName: string;\n startTime: number;\n endTime: number;\n }\n\n export type WebGpuProfilingData = WebGpuProfilingDataV1;\n\n export interface WebGpuFlags {\n /**\n * Set or get the profiling mode.\n *\n * @deprecated Use `env.webgpu.profiling.mode` instead. If `env.webgpu.profiling.mode` is set, this property will be\n * ignored.\n */\n profilingMode?: 'off'|'default';\n /**\n * Set or get the profiling configuration.\n */\n profiling?: {\n /**\n * Set or get the profiling mode.\n *\n * @defaultValue `'off'`\n */\n mode?: 'off'|'default';\n\n /**\n * Set or get a callback function when a profiling data is received. If not set, the profiling data will be\n * printed to console.\n */\n ondata?: (data: WebGpuProfilingData) => void;\n };\n /**\n * Set or get the power preference.\n *\n * Setting this property only has effect before the first WebGPU inference session is created. The value will be\n * used as options for `navigator.gpu.requestAdapter()`.\n *\n * See {@link https://gpuweb.github.io/gpuweb/#dictdef-gpurequestadapteroptions} for more details.\n *\n * @defaultValue `undefined`\n */\n powerPreference?: 'low-power'|'high-performance';\n /**\n * Set or get the force fallback adapter flag.\n *\n * Setting this property only has effect before the first WebGPU inference session is created. The value will be\n * used as options for `navigator.gpu.requestAdapter()`.\n *\n * See {@link https://gpuweb.github.io/gpuweb/#dictdef-gpurequestadapteroptions} for more details.\n *\n * @defaultValue `undefined`\n */\n forceFallbackAdapter?: boolean;\n /**\n * Set or get the adapter for WebGPU.\n *\n * Setting this property only has effect before the first WebGPU inference session is created. The value will be\n * used as the GPU adapter for the underlying WebGPU backend to create GPU device.\n *\n * If this property is not set, it will be available to get after the first WebGPU inference session is created. The\n * value will be the GPU adapter that created by the underlying WebGPU backend.\n *\n * When use with TypeScript, the type of this property is `GPUAdapter` defined in \"@webgpu/types\".\n * Use `const adapter = env.webgpu.adapter as GPUAdapter;` in TypeScript to access this property with correct type.\n *\n * see comments on {@link Tensor.GpuBufferType}\n */\n adapter: unknown;\n /**\n * Get the device for WebGPU.\n *\n * This property is only available after the first WebGPU inference session is created.\n *\n * When use with TypeScript, the type of this property is `GPUDevice` defined in \"@webgpu/types\".\n * Use `const device = env.webgpu.device as GPUDevice;` in TypeScript to access this property with correct type.\n *\n * see comments on {@link Tensor.GpuBufferType} for more details about why not use types defined in \"@webgpu/types\".\n */\n readonly device: unknown;\n /**\n * Set or get whether validate input content.\n *\n * @defaultValue `false`\n */\n validateInputContent?: boolean;\n }\n}\n\nexport interface Env {\n /**\n * set the severity level for logging.\n *\n * @defaultValue `'warning'`\n */\n logLevel?: 'verbose'|'info'|'warning'|'error'|'fatal';\n\n /**\n * Indicate whether run in debug mode.\n *\n * @defaultValue `false`\n */\n debug?: boolean;\n\n /**\n * set or get a boolean value indicating whether to enable trace.\n *\n * @defaultValue `false`\n */\n trace?: boolean;\n\n /**\n * Get version of the current package.\n */\n readonly versions: {\n readonly common: string;\n readonly web?: string;\n readonly node?: string;\n // eslint-disable-next-line @typescript-eslint/naming-convention\n readonly 'react-native'?: string;\n };\n\n /**\n * Represent a set of flags for WebAssembly\n */\n readonly wasm: Env.WebAssemblyFlags;\n\n /**\n * Represent a set of flags for WebGL\n */\n readonly webgl: Env.WebGLFlags;\n\n /**\n * Represent a set of flags for WebGPU\n */\n readonly webgpu: Env.WebGpuFlags;\n\n [name: string]: unknown;\n}\n\n/**\n * Represent a set of flags as a global singleton.\n */\nexport const env: Env = envImpl;\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {TensorToDataUrlOptions, TensorToImageDataOptions} from './tensor-conversion.js';\nimport {Tensor} from './tensor.js';\n\n/**\n * implementation of Tensor.toDataURL()\n */\nexport const tensorToDataURL = (tensor: Tensor, options?: TensorToDataUrlOptions): string => {\n const canvas = typeof document !== 'undefined' ? document.createElement('canvas') : (new OffscreenCanvas(1, 1));\n canvas.width = tensor.dims[3];\n canvas.height = tensor.dims[2];\n const pixels2DContext =\n canvas.getContext('2d') as (CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D | null);\n\n if (pixels2DContext != null) {\n // Default values for height and width & format\n let width: number;\n let height: number;\n if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') {\n width = tensor.dims[2];\n height = tensor.dims[3];\n } else { // Default layout is NCWH\n width = tensor.dims[3];\n height = tensor.dims[2];\n }\n\n const inputformat = options?.format !== undefined ? options.format : 'RGB';\n\n const norm = options?.norm;\n let normMean: [number, number, number, number];\n let normBias: [number, number, number, number];\n if (norm === undefined || norm.mean === undefined) {\n normMean = [255, 255, 255, 255];\n } else {\n if (typeof (norm.mean) === 'number') {\n normMean = [norm.mean, norm.mean, norm.mean, norm.mean];\n } else {\n normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 0];\n if (norm.mean[3] !== undefined) {\n normMean[3] = norm.mean[3];\n }\n }\n }\n if (norm === undefined || norm.bias === undefined) {\n normBias = [0, 0, 0, 0];\n } else {\n if (typeof (norm.bias) === 'number') {\n normBias = [norm.bias, norm.bias, norm.bias, norm.bias];\n } else {\n normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0];\n if (norm.bias[3] !== undefined) {\n normBias[3] = norm.bias[3];\n }\n }\n }\n\n const stride = height * width;\n // Default pointer assignments\n let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1;\n\n // Updating the pointer assignments based on the input image format\n if (inputformat === 'RGBA') {\n rTensorPointer = 0;\n gTensorPointer = stride;\n bTensorPointer = stride * 2;\n aTensorPointer = stride * 3;\n } else if (inputformat === 'RGB') {\n rTensorPointer = 0;\n gTensorPointer = stride;\n bTensorPointer = stride * 2;\n } else if (inputformat === 'RBG') {\n rTensorPointer = 0;\n bTensorPointer = stride;\n gTensorPointer = stride * 2;\n }\n\n for (let i = 0; i < height; i++) {\n for (let j = 0; j < width; j++) {\n const R = ((tensor.data[rTensorPointer++] as number) - normBias[0]) * normMean[0]; // R value\n const G = ((tensor.data[gTensorPointer++] as number) - normBias[1]) * normMean[1]; // G value\n const B = ((tensor.data[bTensorPointer++] as number) - normBias[2]) * normMean[2]; // B value\n const A = aTensorPointer === -1 ?\n 255 :\n ((tensor.data[aTensorPointer++] as number) - normBias[3]) * normMean[3]; // A value\n // eslint-disable-next-line @typescript-eslint/restrict-plus-operands\n pixels2DContext.fillStyle = 'rgba(' + R + ',' + G + ',' + B + ',' + A + ')';\n pixels2DContext.fillRect(j, i, 1, 1);\n }\n }\n if ('toDataURL' in canvas) {\n return canvas.toDataURL();\n } else {\n throw new Error('toDataURL is not supported');\n }\n } else {\n throw new Error('Can not access image data');\n }\n};\n\n/**\n * implementation of Tensor.toImageData()\n */\nexport const tensorToImageData = (tensor: Tensor, options?: TensorToImageDataOptions): ImageData => {\n const pixels2DContext = typeof document !== 'undefined' ?\n document.createElement('canvas').getContext('2d') :\n new OffscreenCanvas(1, 1).getContext('2d') as OffscreenCanvasRenderingContext2D;\n let image: ImageData;\n if (pixels2DContext != null) {\n // Default values for height and width & format\n let width: number;\n let height: number;\n let channels: number;\n if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') {\n width = tensor.dims[2];\n height = tensor.dims[1];\n channels = tensor.dims[3];\n } else { // Default layout is NCWH\n width = tensor.dims[3];\n height = tensor.dims[2];\n channels = tensor.dims[1];\n }\n const inputformat = options !== undefined ? (options.format !== undefined ? options.format : 'RGB') : 'RGB';\n\n const norm = options?.norm;\n let normMean: [number, number, number, number];\n let normBias: [number, number, number, number];\n if (norm === undefined || norm.mean === undefined) {\n normMean = [255, 255, 255, 255];\n } else {\n if (typeof (norm.mean) === 'number') {\n normMean = [norm.mean, norm.mean, norm.mean, norm.mean];\n } else {\n normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 255];\n if (norm.mean[3] !== undefined) {\n normMean[3] = norm.mean[3];\n }\n }\n }\n if (norm === undefined || norm.bias === undefined) {\n normBias = [0, 0, 0, 0];\n } else {\n if (typeof (norm.bias) === 'number') {\n normBias = [norm.bias, norm.bias, norm.bias, norm.bias];\n } else {\n normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0];\n if (norm.bias[3] !== undefined) {\n normBias[3] = norm.bias[3];\n }\n }\n }\n\n const stride = height * width;\n if (options !== undefined) {\n if (options.format !== undefined && (channels === 4 && options.format !== 'RGBA') ||\n (channels === 3 && (options.format !== 'RGB' && options.format !== 'BGR'))) {\n throw new Error('Tensor format doesn\\'t match input tensor dims');\n }\n }\n\n // Default pointer assignments\n const step = 4;\n let rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3;\n let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1;\n\n // Updating the pointer assignments based on the input image format\n if (inputformat === 'RGBA') {\n rTensorPointer = 0;\n gTensorPointer = stride;\n bTensorPointer = stride * 2;\n aTensorPointer = stride * 3;\n } else if (inputformat === 'RGB') {\n rTensorPointer = 0;\n gTensorPointer = stride;\n bTensorPointer = stride * 2;\n } else if (inputformat === 'RBG') {\n rTensorPointer = 0;\n bTensorPointer = stride;\n gTensorPointer = stride * 2;\n }\n\n image = pixels2DContext.createImageData(width, height);\n\n for (let i = 0; i < height * width;\n rImagePointer += step, gImagePointer += step, bImagePointer += step, aImagePointer += step, i++) {\n image.data[rImagePointer] = ((tensor.data[rTensorPointer++] as number) - normBias[0]) * normMean[0]; // R value\n image.data[gImagePointer] = ((tensor.data[gTensorPointer++] as number) - normBias[1]) * normMean[1]; // G value\n image.data[bImagePointer] = ((tensor.data[bTensorPointer++] as number) - normBias[2]) * normMean[2]; // B value\n image.data[aImagePointer] = aTensorPointer === -1 ?\n 255 :\n ((tensor.data[aTensorPointer++] as number) - normBias[3]) * normMean[3]; // A value\n }\n\n } else {\n throw new Error('Can not access image data');\n }\n return image;\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {OptionsDimensions, OptionsFormat, OptionsNormalizationParameters, OptionsTensorFormat, OptionsTensorLayout, TensorFromGpuBufferOptions, TensorFromImageBitmapOptions, TensorFromImageDataOptions, TensorFromImageElementOptions, TensorFromTextureOptions, TensorFromUrlOptions} from './tensor-factory.js';\nimport {Tensor} from './tensor-impl.js';\nimport {Tensor as TensorInterface} from './tensor.js';\n\ninterface BufferToTensorOptions extends OptionsDimensions, OptionsTensorLayout, OptionsNormalizationParameters,\n OptionsFormat, OptionsTensorFormat {}\n\n/**\n * Create a new tensor object from image object\n *\n * @param buffer - Extracted image buffer data - assuming RGBA format\n * @param imageFormat - input image configuration - required configurations height, width, format\n * @param tensorFormat - output tensor configuration - Default is RGB format\n */\nexport const bufferToTensor = (buffer: Uint8ClampedArray|undefined, options: BufferToTensorOptions): Tensor => {\n if (buffer === undefined) {\n throw new Error('Image buffer must be defined');\n }\n if (options.height === undefined || options.width === undefined) {\n throw new Error('Image height and width must be defined');\n }\n if (options.tensorLayout === 'NHWC') {\n throw new Error('NHWC Tensor layout is not supported yet');\n }\n\n const {height, width} = options;\n\n const norm = options.norm ?? {mean: 255, bias: 0};\n let normMean: [number, number, number, number];\n let normBias: [number, number, number, number];\n\n if (typeof (norm.mean) === 'number') {\n normMean = [norm.mean, norm.mean, norm.mean, norm.mean];\n } else {\n normMean = [norm.mean![0], norm.mean![1], norm.mean![2], norm.mean![3] ?? 255];\n }\n\n if (typeof (norm.bias) === 'number') {\n normBias = [norm.bias, norm.bias, norm.bias, norm.bias];\n } else {\n normBias = [norm.bias![0], norm.bias![1], norm.bias![2], norm.bias![3] ?? 0];\n }\n\n const inputformat = options.format !== undefined ? options.format : 'RGBA';\n // default value is RGBA since imagedata and HTMLImageElement uses it\n\n const outputformat =\n options.tensorFormat !== undefined ? (options.tensorFormat !== undefined ? options.tensorFormat : 'RGB') : 'RGB';\n const stride = height * width;\n const float32Data = outputformat === 'RGBA' ? new Float32Array(stride * 4) : new Float32Array(stride * 3);\n\n // Default pointer assignments\n let step = 4, rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3;\n let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1;\n\n // Updating the pointer assignments based on the input image format\n if (inputformat === 'RGB') {\n step = 3;\n rImagePointer = 0;\n gImagePointer = 1;\n bImagePointer = 2;\n aImagePointer = -1;\n }\n\n // Updating the pointer assignments based on the output tensor format\n if (outputformat === 'RGBA') {\n aTensorPointer = stride * 3;\n } else if (outputformat === 'RBG') {\n rTensorPointer = 0;\n bTensorPointer = stride;\n gTensorPointer = stride * 2;\n } else if (outputformat === 'BGR') {\n bTensorPointer = 0;\n gTensorPointer = stride;\n rTensorPointer = stride * 2;\n }\n\n for (let i = 0; i < stride;\n i++, rImagePointer += step, bImagePointer += step, gImagePointer += step, aImagePointer += step) {\n float32Data[rTensorPointer++] = (buffer[rImagePointer] + normBias[0]) / normMean[0];\n float32Data[gTensorPointer++] = (buffer[gImagePointer] + normBias[1]) / normMean[1];\n float32Data[bTensorPointer++] = (buffer[bImagePointer] + normBias[2]) / normMean[2];\n if (aTensorPointer !== -1 && aImagePointer !== -1) {\n float32Data[aTensorPointer++] = (buffer[aImagePointer] + normBias[3]) / normMean[3];\n }\n }\n\n // Float32Array -> ort.Tensor\n const outputTensor = outputformat === 'RGBA' ? new Tensor('float32', float32Data, [1, 4, height, width]) :\n new Tensor('float32', float32Data, [1, 3, height, width]);\n return outputTensor;\n};\n\n/**\n * implementation of Tensor.fromImage().\n */\nexport const tensorFromImage = async(\n image: ImageData|HTMLImageElement|ImageBitmap|string,\n options?: TensorFromImageDataOptions|TensorFromImageElementOptions|TensorFromImageBitmapOptions|\n TensorFromUrlOptions): Promise => {\n // checking the type of image object\n const isHTMLImageEle = typeof (HTMLImageElement) !== 'undefined' && image instanceof HTMLImageElement;\n const isImageDataEle = typeof (ImageData) !== 'undefined' && image instanceof ImageData;\n const isImageBitmap = typeof (ImageBitmap) !== 'undefined' && image instanceof ImageBitmap;\n const isString = typeof image === 'string';\n\n let data: Uint8ClampedArray|undefined;\n let bufferToTensorOptions: BufferToTensorOptions = options ?? {};\n\n const createCanvas = () => {\n if (typeof document !== 'undefined') {\n return document.createElement('canvas');\n } else if (typeof OffscreenCanvas !== 'undefined') {\n return new OffscreenCanvas(1, 1);\n } else {\n throw new Error('Canvas is not supported');\n }\n };\n const createCanvasContext = (canvas: HTMLCanvasElement|OffscreenCanvas) => {\n if (canvas instanceof HTMLCanvasElement) {\n return canvas.getContext('2d');\n } else if (canvas instanceof OffscreenCanvas) {\n return canvas.getContext('2d') as OffscreenCanvasRenderingContext2D;\n } else {\n return null;\n }\n };\n // filling and checking image configuration options\n if (isHTMLImageEle) {\n // HTMLImageElement - image object - format is RGBA by default\n const canvas = createCanvas();\n canvas.width = image.width;\n canvas.height = image.height;\n const pixels2DContext = createCanvasContext(canvas);\n\n if (pixels2DContext != null) {\n let height = image.height;\n let width = image.width;\n if (options !== undefined && options.resizedHeight !== undefined && options.resizedWidth !== undefined) {\n height = options.resizedHeight;\n width = options.resizedWidth;\n }\n\n if (options !== undefined) {\n bufferToTensorOptions = options;\n if (options.tensorFormat !== undefined) {\n throw new Error('Image input config format must be RGBA for HTMLImageElement');\n } else {\n bufferToTensorOptions.tensorFormat = 'RGBA';\n }\n bufferToTensorOptions.height = height;\n bufferToTensorOptions.width = width;\n } else {\n bufferToTensorOptions.tensorFormat = 'RGBA';\n bufferToTensorOptions.height = height;\n bufferToTensorOptions.width = width;\n }\n\n pixels2DContext.drawImage(image, 0, 0);\n data = pixels2DContext.getImageData(0, 0, width, height).data;\n } else {\n throw new Error('Can not access image data');\n }\n } else if (isImageDataEle) {\n let height: number;\n let width: number;\n\n if (options !== undefined && options.resizedWidth !== undefined && options.resizedHeight !== undefined) {\n height = options.resizedHeight;\n width = options.resizedWidth;\n } else {\n height = image.height;\n width = image.width;\n }\n\n if (options !== undefined) {\n bufferToTensorOptions = options;\n }\n bufferToTensorOptions.format = 'RGBA';\n bufferToTensorOptions.height = height;\n bufferToTensorOptions.width = width;\n\n if (options !== undefined) {\n const tempCanvas = createCanvas();\n\n tempCanvas.width = width;\n tempCanvas.height = height;\n\n const pixels2DContext = createCanvasContext(tempCanvas);\n\n if (pixels2DContext != null) {\n pixels2DContext.putImageData(image, 0, 0);\n data = pixels2DContext.getImageData(0, 0, width, height).data;\n } else {\n throw new Error('Can not access image data');\n }\n } else {\n data = image.data;\n }\n } else if (isImageBitmap) {\n // ImageBitmap - image object - format must be provided by user\n if (options === undefined) {\n throw new Error('Please provide image config with format for Imagebitmap');\n }\n\n const canvas = createCanvas();\n canvas.width = image.width;\n canvas.height = image.height;\n const pixels2DContext = createCanvasContext(canvas);\n\n if (pixels2DContext != null) {\n const height = image.height;\n const width = image.width;\n pixels2DContext.drawImage(image, 0, 0, width, height);\n data = pixels2DContext.getImageData(0, 0, width, height).data;\n bufferToTensorOptions.height = height;\n bufferToTensorOptions.width = width;\n return bufferToTensor(data, bufferToTensorOptions);\n } else {\n throw new Error('Can not access image data');\n }\n } else if (isString) {\n return new Promise((resolve, reject) => {\n const canvas = createCanvas();\n const context = createCanvasContext(canvas);\n if (!image || !context) {\n return reject();\n }\n const newImage = new Image();\n newImage.crossOrigin = 'Anonymous';\n newImage.src = image;\n newImage.onload = () => {\n canvas.width = newImage.width;\n canvas.height = newImage.height;\n context.drawImage(newImage, 0, 0, canvas.width, canvas.height);\n const img = context.getImageData(0, 0, canvas.width, canvas.height);\n\n bufferToTensorOptions.height = canvas.height;\n bufferToTensorOptions.width = canvas.width;\n resolve(bufferToTensor(img.data, bufferToTensorOptions));\n };\n });\n } else {\n throw new Error('Input data provided is not supported - aborted tensor creation');\n }\n\n if (data !== undefined) {\n return bufferToTensor(data, bufferToTensorOptions);\n } else {\n throw new Error('Input data provided is not supported - aborted tensor creation');\n }\n};\n\n/**\n * implementation of Tensor.fromTexture().\n */\nexport const tensorFromTexture = (\n texture: TensorInterface.TextureType, options: TensorFromTextureOptions): Tensor => {\n const {width, height, download, dispose} = options;\n // Always assume RGBAF32. TODO: support different texture format\n const dims = [1, height, width, 4];\n return new Tensor({location: 'texture', type: 'float32', texture, dims, download, dispose});\n};\n\n/**\n * implementation of Tensor.fromGpuBuffer().\n */\nexport const tensorFromGpuBuffer = (\n gpuBuffer: TensorInterface.GpuBufferType, options: TensorFromGpuBufferOptions): Tensor => {\n const {dataType, dims, download, dispose} = options;\n return new Tensor({location: 'gpu-buffer', type: dataType ?? 'float32', gpuBuffer, dims, download, dispose});\n};\n\n/**\n * implementation of Tensor.fromPinnedBuffer().\n */\nexport const tensorFromPinnedBuffer = (\n type: T, buffer: TensorInterface.DataTypeMap[T], dims?: readonly number[]): Tensor =>\n new Tensor({location: 'cpu-pinned', type, data: buffer, dims: dims ?? [buffer.length]});\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from './tensor.js';\n\nexport type SupportedTypedArrayConstructors = Float32ArrayConstructor|Uint8ArrayConstructor|Int8ArrayConstructor|\n Uint16ArrayConstructor|Int16ArrayConstructor|Int32ArrayConstructor|BigInt64ArrayConstructor|Uint8ArrayConstructor|\n Float64ArrayConstructor|Uint32ArrayConstructor|BigUint64ArrayConstructor;\nexport type SupportedTypedArray = InstanceType;\n\n// a runtime map that maps type string to TypedArray constructor. Should match Tensor.DataTypeMap.\nexport const NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP = new Map([\n ['float32', Float32Array],\n ['uint8', Uint8Array],\n ['int8', Int8Array],\n ['uint16', Uint16Array],\n ['int16', Int16Array],\n ['int32', Int32Array],\n ['bool', Uint8Array],\n ['float64', Float64Array],\n ['uint32', Uint32Array],\n]);\n\n// a runtime map that maps type string to TypedArray constructor. Should match Tensor.DataTypeMap.\nexport const NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP = new Map([\n [Float32Array, 'float32'],\n [Uint8Array, 'uint8'],\n [Int8Array, 'int8'],\n [Uint16Array, 'uint16'],\n [Int16Array, 'int16'],\n [Int32Array, 'int32'],\n [Float64Array, 'float64'],\n [Uint32Array, 'uint32'],\n]);\n\n// a dummy type declaration for Float16Array in case any polyfill is available.\ndeclare global {\n // eslint-disable-next-line @typescript-eslint/naming-convention, @typescript-eslint/no-explicit-any\n const Float16Array: any;\n}\n\n// the following code allows delaying execution of BigInt/Float16Array checking. This allows lazy initialization for\n// NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP and NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, which allows BigInt/Float16Array\n// polyfill if available.\nlet isTypedArrayChecked = false;\nexport const checkTypedArray = () => {\n if (!isTypedArrayChecked) {\n isTypedArrayChecked = true;\n const isBigInt64ArrayAvailable = typeof BigInt64Array !== 'undefined' && BigInt64Array.from;\n const isBigUint64ArrayAvailable = typeof BigUint64Array !== 'undefined' && BigUint64Array.from;\n const isFloat16ArrayAvailable = typeof Float16Array !== 'undefined' && Float16Array.from;\n\n if (isBigInt64ArrayAvailable) {\n NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('int64', BigInt64Array);\n NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array, 'int64');\n }\n if (isBigUint64ArrayAvailable) {\n NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('uint64', BigUint64Array);\n NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array, 'uint64');\n }\n if (isFloat16ArrayAvailable) {\n NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('float16', Float16Array);\n NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(Float16Array, 'float16');\n } else {\n // if Float16Array is not available, use 'Uint16Array' to store the data.\n NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('float16', Uint16Array);\n }\n }\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {CpuPinnedConstructorParameters, GpuBufferConstructorParameters, TextureConstructorParameters} from './tensor-factory.js';\nimport {Tensor} from './tensor-impl.js';\n\n/**\n * calculate size from dims.\n *\n * @param dims the dims array. May be an illegal input.\n */\nexport const calculateSize = (dims: readonly unknown[]): number => {\n let size = 1;\n for (let i = 0; i < dims.length; i++) {\n const dim = dims[i];\n if (typeof dim !== 'number' || !Number.isSafeInteger(dim)) {\n throw new TypeError(`dims[${i}] must be an integer, got: ${dim}`);\n }\n if (dim < 0) {\n throw new RangeError(`dims[${i}] must be a non-negative integer, got: ${dim}`);\n }\n size *= dim;\n }\n return size;\n};\n\n/**\n * implementation of Tensor.reshape()\n */\nexport const tensorReshape = (tensor: Tensor, dims: readonly number[]): Tensor => {\n switch (tensor.location) {\n case 'cpu':\n return new Tensor(tensor.type, tensor.data, dims);\n case 'cpu-pinned':\n return new Tensor({\n location: 'cpu-pinned',\n data: tensor.data as CpuPinnedConstructorParameters['data'],\n type: tensor.type as CpuPinnedConstructorParameters['type'],\n dims,\n });\n case 'texture':\n return new Tensor({\n location: 'texture',\n texture: tensor.texture,\n type: tensor.type as TextureConstructorParameters['type'],\n dims,\n });\n case 'gpu-buffer':\n return new Tensor({\n location: 'gpu-buffer',\n gpuBuffer: tensor.gpuBuffer,\n type: tensor.type as GpuBufferConstructorParameters['type'],\n dims,\n });\n default:\n throw new Error(`tensorReshape: tensor location ${tensor.location} is not supported`);\n }\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {tensorToDataURL, tensorToImageData} from './tensor-conversion-impl.js';\nimport {TensorToDataUrlOptions, TensorToImageDataOptions} from './tensor-conversion.js';\nimport {tensorFromGpuBuffer, tensorFromImage, tensorFromPinnedBuffer, tensorFromTexture} from './tensor-factory-impl.js';\nimport {CpuPinnedConstructorParameters, GpuBufferConstructorParameters, TensorFromGpuBufferOptions, TensorFromImageBitmapOptions, TensorFromImageDataOptions, TensorFromImageElementOptions, TensorFromTextureOptions, TensorFromUrlOptions, TextureConstructorParameters} from './tensor-factory.js';\nimport {checkTypedArray, NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP, NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, SupportedTypedArray, SupportedTypedArrayConstructors} from './tensor-impl-type-mapping.js';\nimport {calculateSize, tensorReshape} from './tensor-utils-impl.js';\nimport {Tensor as TensorInterface} from './tensor.js';\n\n// type aliases for those exported from Tensor interface\n\ntype TensorType = TensorInterface.Type;\ntype TensorDataType = TensorInterface.DataType;\ntype TensorDataLocation = TensorInterface.DataLocation;\ntype TensorTextureType = TensorInterface.TextureType;\ntype TensorGpuBufferType = TensorInterface.GpuBufferType;\n\n/**\n * the implementation of Tensor interface.\n *\n * @ignore\n */\nexport class Tensor implements TensorInterface {\n // #region constructors\n\n /**\n * Construct a new CPU tensor object from the given type, data and dims.\n */\n constructor(\n type: TensorType, data: TensorDataType|readonly string[]|readonly number[]|readonly boolean[],\n dims?: readonly number[]);\n /**\n * Construct a new CPU tensor object from the given data and dims. Type is inferred from data.\n */\n constructor(data: TensorDataType|readonly string[]|readonly boolean[], dims?: readonly number[]);\n /**\n * Construct a new tensor object from the pinned CPU data with the given type and dims.\n *\n * Tensor's location will be set to 'cpu-pinned'.\n *\n * @param params - Specify the parameters to construct the tensor.\n */\n constructor(params: CpuPinnedConstructorParameters);\n /**\n * Construct a new tensor object from the WebGL texture with the given type and dims.\n *\n * Tensor's location will be set to 'texture'.\n *\n * @param params - Specify the parameters to construct the tensor.\n */\n constructor(params: TextureConstructorParameters);\n /**\n * Construct a new tensor object from the WebGPU buffer with the given type and dims.\n *\n * Tensor's location will be set to 'gpu-buffer'.\n *\n * @param params - Specify the parameters to construct the tensor.\n */\n constructor(params: GpuBufferConstructorParameters);\n\n /**\n * implementation.\n */\n constructor(\n arg0: TensorType|TensorDataType|readonly string[]|readonly boolean[]|CpuPinnedConstructorParameters|\n TextureConstructorParameters|GpuBufferConstructorParameters,\n arg1?: TensorDataType|readonly number[]|readonly string[]|readonly boolean[], arg2?: readonly number[]) {\n // perform one-time check for BigInt/Float16Array support\n checkTypedArray();\n\n let type: TensorType;\n let dims: readonly number[];\n\n if (typeof arg0 === 'object' && 'location' in arg0) {\n //\n // constructing tensor from specific location\n //\n this.dataLocation = arg0.location;\n type = arg0.type;\n dims = arg0.dims;\n switch (arg0.location) {\n case 'cpu-pinned': {\n const expectedTypedArrayConstructor = NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(type);\n if (!expectedTypedArrayConstructor) {\n throw new TypeError(`unsupported type \"${type}\" to create tensor from pinned buffer`);\n }\n if (!(arg0.data instanceof expectedTypedArrayConstructor)) {\n throw new TypeError(`buffer should be of type ${expectedTypedArrayConstructor.name}`);\n }\n this.cpuData = arg0.data;\n break;\n }\n case 'texture': {\n if (type !== 'float32') {\n throw new TypeError(`unsupported type \"${type}\" to create tensor from texture`);\n }\n this.gpuTextureData = arg0.texture;\n this.downloader = arg0.download;\n this.disposer = arg0.dispose;\n break;\n }\n case 'gpu-buffer': {\n if ((type !== 'float32' && type !== 'float16' && type !== 'int32' && type !== 'int64' && type !== 'uint32' &&\n type !== 'uint8' && type !== 'bool')) {\n throw new TypeError(`unsupported type \"${type}\" to create tensor from gpu buffer`);\n }\n this.gpuBufferData = arg0.gpuBuffer;\n this.downloader = arg0.download;\n this.disposer = arg0.dispose;\n break;\n }\n default:\n throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`);\n }\n } else {\n //\n // constructing tensor of location 'cpu'\n //\n let data: TensorDataType;\n let maybeDims: typeof arg1|typeof arg2;\n // check whether arg0 is type or data\n if (typeof arg0 === 'string') {\n //\n // Override: constructor(type, data, ...)\n //\n type = arg0;\n maybeDims = arg2;\n if (arg0 === 'string') {\n // string tensor\n if (!Array.isArray(arg1)) {\n throw new TypeError('A string tensor\\'s data must be a string array.');\n }\n // we don't check whether every element in the array is string; this is too slow. we assume it's correct and\n // error will be populated at inference\n data = arg1;\n } else {\n // numeric tensor\n const typedArrayConstructor = NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(arg0);\n if (typedArrayConstructor === undefined) {\n throw new TypeError(`Unsupported tensor type: ${arg0}.`);\n }\n if (Array.isArray(arg1)) {\n if (arg0 === 'float16' && typedArrayConstructor === Uint16Array) {\n // When no Float16Array polyfill is used, we cannot create 'float16' tensor from number array.\n //\n // Throw error here because when user try to use number array as data,\n // e.g. new Tensor('float16', [1, 2, 3, 4], dims)), it will actually call\n // Uint16Array.from(arg1) which generates wrong data.\n throw new TypeError(\n 'Creating a float16 tensor from number array is not supported. Please use Uint16Array as data.');\n } else if (arg0 === 'uint64' || arg0 === 'int64') {\n // use 'as any' here because:\n // 1. TypeScript's check on type of 'Array.isArray()' does not work with readonly arrays.\n // see https://github.com/microsoft/TypeScript/issues/17002\n // 2. TypeScript's check on union type of '(BigInt64ArrayConstructor|BigUint64ArrayConstructor).from()'\n // does not accept parameter mapFn.\n // 3. parameters of 'SupportedTypedArrayConstructors.from()' does not match the requirement of the union\n // type.\n\n // assume 'arg1' is of type \"readonly number[]|readonly bigint[]\" here.\n\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n data = (typedArrayConstructor as any).from(arg1, BigInt);\n } else {\n // assume 'arg1' is of type \"readonly number[]\" here.\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n data = (typedArrayConstructor as any).from(arg1);\n }\n } else if (arg1 instanceof typedArrayConstructor) {\n data = arg1;\n } else {\n throw new TypeError(`A ${type} tensor's data must be type of ${typedArrayConstructor}`);\n }\n }\n } else {\n //\n // Override: constructor(data, ...)\n //\n maybeDims = arg1;\n if (Array.isArray(arg0)) {\n // only boolean[] and string[] is supported\n if (arg0.length === 0) {\n throw new TypeError('Tensor type cannot be inferred from an empty array.');\n }\n const firstElementType = typeof arg0[0];\n if (firstElementType === 'string') {\n type = 'string';\n data = arg0;\n } else if (firstElementType === 'boolean') {\n type = 'bool';\n // 'arg0' is of type 'boolean[]'. Uint8Array.from(boolean[]) actually works, but typescript thinks this is\n // wrong type. We use 'as any' to make it happy.\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n data = Uint8Array.from(arg0 as any[]);\n } else {\n throw new TypeError(`Invalid element type of data array: ${firstElementType}.`);\n }\n } else {\n // get tensor type from TypedArray\n const mappedType =\n NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(arg0.constructor as SupportedTypedArrayConstructors);\n if (mappedType === undefined) {\n throw new TypeError(`Unsupported type for tensor data: ${arg0.constructor}.`);\n }\n type = mappedType;\n data = arg0 as SupportedTypedArray;\n }\n }\n\n // type and data is processed, now processing dims\n if (maybeDims === undefined) {\n // assume 1-D tensor if dims omitted\n maybeDims = [data.length];\n } else if (!Array.isArray(maybeDims)) {\n throw new TypeError('A tensor\\'s dims must be a number array');\n }\n dims = maybeDims as readonly number[];\n\n this.cpuData = data;\n this.dataLocation = 'cpu';\n }\n\n // perform check on dims\n const size = calculateSize(dims);\n // if data is on CPU, check whether data length matches tensor size\n if (this.cpuData && size !== this.cpuData.length) {\n throw new Error(`Tensor's size(${size}) does not match data length(${this.cpuData.length}).`);\n }\n\n this.type = type;\n this.dims = dims;\n this.size = size;\n }\n // #endregion\n\n // #region factory\n static async fromImage(\n image: ImageData|HTMLImageElement|ImageBitmap|string,\n options?: TensorFromImageDataOptions|TensorFromImageElementOptions|TensorFromImageBitmapOptions|\n TensorFromUrlOptions): Promise {\n return tensorFromImage(image, options);\n }\n\n static fromTexture(\n texture: TensorTextureType, options: TensorFromTextureOptions): TensorInterface {\n return tensorFromTexture(texture, options);\n }\n\n static fromGpuBuffer(\n gpuBuffer: TensorGpuBufferType, options: TensorFromGpuBufferOptions): TensorInterface {\n return tensorFromGpuBuffer(gpuBuffer, options);\n }\n\n static fromPinnedBuffer(\n type: T, buffer: TensorInterface.DataTypeMap[T], dims?: readonly number[]): Tensor {\n return tensorFromPinnedBuffer(type, buffer, dims);\n }\n\n // #endregion\n\n // #region conversions\n toDataURL(options?: TensorToDataUrlOptions): string {\n return tensorToDataURL(this, options);\n }\n\n toImageData(options?: TensorToImageDataOptions): ImageData {\n return tensorToImageData(this, options);\n }\n // #endregion\n\n // #region public fields\n readonly dims: readonly number[];\n readonly type: TensorType;\n readonly size: number;\n // #endregion\n\n // #region private fields\n\n /**\n * stores the location of the data.\n */\n private dataLocation: TensorDataLocation;\n\n /**\n * stores the data on CPU, if location is 'cpu' or 'cpu-pinned'. otherwise empty.\n */\n private cpuData?: TensorDataType;\n\n /**\n * stores the underlying texture when location is 'texture'. otherwise empty.\n */\n private gpuTextureData?: TensorTextureType;\n\n /**\n * stores the underlying GPU buffer when location is 'gpu-buffer'. otherwise empty.\n */\n private gpuBufferData?: TensorGpuBufferType;\n\n /**\n * stores an optional downloader function to download data from GPU to CPU.\n */\n private downloader?(): Promise;\n\n /**\n * a flag indicating whether the data is being downloaded from GPU to CPU.\n */\n private isDownloading?: boolean;\n\n /**\n * stores an optional disposer function to dispose the underlying data.\n */\n private disposer?(): void;\n // #endregion\n\n // #region properties\n get data(): TensorDataType {\n this.ensureValid();\n if (!this.cpuData) {\n throw new Error(\n 'The data is not on CPU. Use `getData()` to download GPU data to CPU, ' +\n 'or use `texture` or `gpuBuffer` property to access the GPU data directly.');\n }\n return this.cpuData;\n }\n\n get location(): TensorDataLocation {\n return this.dataLocation;\n }\n\n get texture(): TensorTextureType {\n this.ensureValid();\n if (!this.gpuTextureData) {\n throw new Error('The data is not stored as a WebGL texture.');\n }\n return this.gpuTextureData;\n }\n\n get gpuBuffer(): TensorGpuBufferType {\n this.ensureValid();\n if (!this.gpuBufferData) {\n throw new Error('The data is not stored as a WebGPU buffer.');\n }\n return this.gpuBufferData;\n }\n // #endregion\n\n // #region methods\n\n async getData(releaseData?: boolean): Promise {\n this.ensureValid();\n switch (this.dataLocation) {\n case 'cpu':\n case 'cpu-pinned':\n return this.data;\n case 'texture':\n case 'gpu-buffer': {\n if (!this.downloader) {\n throw new Error('The current tensor is not created with a specified data downloader.');\n }\n if (this.isDownloading) {\n throw new Error('The current tensor is being downloaded.');\n }\n try {\n this.isDownloading = true;\n const data = await this.downloader();\n this.downloader = undefined;\n this.dataLocation = 'cpu';\n this.cpuData = data;\n\n if (releaseData && this.disposer) {\n this.disposer();\n this.disposer = undefined;\n }\n\n return data;\n\n } finally {\n this.isDownloading = false;\n }\n }\n default:\n throw new Error(`cannot get data from location: ${this.dataLocation}`);\n }\n }\n\n dispose(): void {\n if (this.isDownloading) {\n throw new Error('The current tensor is being downloaded.');\n }\n\n if (this.disposer) {\n this.disposer();\n this.disposer = undefined;\n }\n this.cpuData = undefined;\n this.gpuTextureData = undefined;\n this.gpuBufferData = undefined;\n this.downloader = undefined;\n this.isDownloading = undefined;\n\n this.dataLocation = 'none';\n }\n\n // #endregion\n\n // #region tensor utilities\n private ensureValid(): void {\n if (this.dataLocation === 'none') {\n throw new Error('The tensor is disposed.');\n }\n }\n\n reshape(dims: readonly number[]): TensorInterface {\n this.ensureValid();\n if (this.downloader || this.disposer) {\n throw new Error('Cannot reshape a tensor that owns GPU resource.');\n }\n return tensorReshape(this, dims);\n }\n // #endregion\n}\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {TensorFactory} from './tensor-factory.js';\nimport {Tensor as TensorImpl} from './tensor-impl.js';\nimport {TypedTensorUtils} from './tensor-utils.js';\n\n/* eslint-disable @typescript-eslint/no-redeclare */\n\n/**\n * represent a basic tensor with specified dimensions and data type.\n */\ninterface TypedTensorBase {\n /**\n * Get the dimensions of the tensor.\n */\n readonly dims: readonly number[];\n /**\n * Get the data type of the tensor.\n */\n readonly type: T;\n /**\n * Get the buffer data of the tensor.\n *\n * If the data is not on CPU (eg. it's in the form of WebGL texture or WebGPU buffer), throw error.\n */\n readonly data: Tensor.DataTypeMap[T];\n /**\n * Get the location of the data.\n */\n readonly location: Tensor.DataLocation;\n /**\n * Get the WebGL texture that holds the tensor data.\n *\n * If the data is not on GPU as WebGL texture, throw error.\n */\n readonly texture: Tensor.TextureType;\n /**\n * Get the WebGPU buffer that holds the tensor data.\n *\n * If the data is not on GPU as WebGPU buffer, throw error.\n */\n readonly gpuBuffer: Tensor.GpuBufferType;\n\n /**\n * Get the buffer data of the tensor.\n *\n * If the data is on CPU, returns the data immediately.\n * If the data is on GPU, downloads the data and returns the promise.\n *\n * @param releaseData - whether release the data on GPU. Ignore if data is already on CPU.\n */\n getData(releaseData?: boolean): Promise;\n\n /**\n * Dispose the tensor data.\n *\n * If the data is on CPU, remove its internal reference to the underlying data.\n * If the data is on GPU, release the data on GPU.\n *\n * After calling this function, the tensor is considered no longer valid. Its location will be set to 'none'.\n */\n dispose(): void;\n}\n\nexport declare namespace Tensor {\n interface DataTypeMap {\n float32: Float32Array;\n uint8: Uint8Array;\n int8: Int8Array;\n uint16: Uint16Array;\n int16: Int16Array;\n int32: Int32Array;\n int64: BigInt64Array;\n string: string[];\n bool: Uint8Array;\n float16: Uint16Array; // Keep using Uint16Array until we have a concrete solution for float 16.\n float64: Float64Array;\n uint32: Uint32Array;\n uint64: BigUint64Array;\n // complex64: never;\n // complex128: never;\n // bfloat16: never;\n }\n\n interface ElementTypeMap {\n float32: number;\n uint8: number;\n int8: number;\n uint16: number;\n int16: number;\n int32: number;\n int64: bigint;\n string: string;\n bool: boolean;\n float16: number; // Keep using Uint16Array until we have a concrete solution for float 16.\n float64: number;\n uint32: number;\n uint64: bigint;\n // complex64: never;\n // complex128: never;\n // bfloat16: never;\n }\n\n type DataType = DataTypeMap[Type];\n type ElementType = ElementTypeMap[Type];\n\n /**\n * supported data types for constructing a tensor from a pinned CPU buffer\n */\n export type CpuPinnedDataTypes = Exclude;\n\n /**\n * type alias for WebGL texture\n */\n export type TextureType = WebGLTexture;\n\n /**\n * supported data types for constructing a tensor from a WebGL texture\n */\n export type TextureDataTypes = 'float32';\n\n /**\n * type alias for WebGPU buffer\n *\n * The reason why we don't use type \"GPUBuffer\" defined in webgpu.d.ts from @webgpu/types is because \"@webgpu/types\"\n * requires \"@types/dom-webcodecs\" as peer dependency when using TypeScript < v5.1 and its version need to be chosen\n * carefully according to the TypeScript version being used. This means so far there is not a way to keep every\n * TypeScript version happy. It turns out that we will easily broke users on some TypeScript version.\n *\n * for more info see https://github.com/gpuweb/types/issues/127\n */\n export type GpuBufferType = {size: number; mapState: 'unmapped' | 'pending' | 'mapped'};\n\n /**\n * supported data types for constructing a tensor from a WebGPU buffer\n */\n export type GpuBufferDataTypes = 'float32'|'float16'|'int32'|'int64'|'uint32'|'uint8'|'bool';\n\n /**\n * represent where the tensor data is stored\n */\n export type DataLocation = 'none'|'cpu'|'cpu-pinned'|'texture'|'gpu-buffer';\n\n /**\n * represent the data type of a tensor\n */\n export type Type = keyof DataTypeMap;\n}\n\n/**\n * Represent multi-dimensional arrays to feed to or fetch from model inferencing.\n */\nexport interface TypedTensor extends TypedTensorBase, TypedTensorUtils {}\n/**\n * Represent multi-dimensional arrays to feed to or fetch from model inferencing.\n */\nexport interface Tensor extends TypedTensorBase, TypedTensorUtils {}\n\n/**\n * type TensorConstructor defines the constructors of 'Tensor' to create CPU tensor instances.\n */\nexport interface TensorConstructor extends TensorFactory {\n // #region CPU tensor - specify element type\n /**\n * Construct a new string tensor object from the given type, data and dims.\n *\n * @param type - Specify the element type.\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(type: 'string', data: Tensor.DataTypeMap['string']|readonly string[],\n dims?: readonly number[]): TypedTensor<'string'>;\n\n /**\n * Construct a new bool tensor object from the given type, data and dims.\n *\n * @param type - Specify the element type.\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(type: 'bool', data: Tensor.DataTypeMap['bool']|readonly boolean[], dims?: readonly number[]): TypedTensor<'bool'>;\n\n /**\n * Construct a new 64-bit integer typed tensor object from the given type, data and dims.\n *\n * @param type - Specify the element type.\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(\n type: T, data: Tensor.DataTypeMap[T]|readonly bigint[]|readonly number[],\n dims?: readonly number[]): TypedTensor;\n\n /**\n * Construct a new numeric tensor object from the given type, data and dims.\n *\n * @param type - Specify the element type.\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new>(\n type: T, data: Tensor.DataTypeMap[T]|readonly number[], dims?: readonly number[]): TypedTensor;\n // #endregion\n\n // #region CPU tensor - infer element types\n\n /**\n * Construct a new float32 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Float32Array, dims?: readonly number[]): TypedTensor<'float32'>;\n\n /**\n * Construct a new int8 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Int8Array, dims?: readonly number[]): TypedTensor<'int8'>;\n\n /**\n * Construct a new uint8 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Uint8Array, dims?: readonly number[]): TypedTensor<'uint8'>;\n\n /**\n * Construct a new uint16 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Uint16Array, dims?: readonly number[]): TypedTensor<'uint16'>;\n\n /**\n * Construct a new int16 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Int16Array, dims?: readonly number[]): TypedTensor<'int16'>;\n\n /**\n * Construct a new int32 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Int32Array, dims?: readonly number[]): TypedTensor<'int32'>;\n\n /**\n * Construct a new int64 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: BigInt64Array, dims?: readonly number[]): TypedTensor<'int64'>;\n\n /**\n * Construct a new string tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: readonly string[], dims?: readonly number[]): TypedTensor<'string'>;\n\n /**\n * Construct a new bool tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: readonly boolean[], dims?: readonly number[]): TypedTensor<'bool'>;\n\n /**\n * Construct a new float64 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Float64Array, dims?: readonly number[]): TypedTensor<'float64'>;\n\n /**\n * Construct a new uint32 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Uint32Array, dims?: readonly number[]): TypedTensor<'uint32'>;\n\n /**\n * Construct a new uint64 tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: BigUint64Array, dims?: readonly number[]): TypedTensor<'uint64'>;\n\n // #endregion\n\n // #region CPU tensor - fall back to non-generic tensor type declaration\n\n /**\n * Construct a new tensor object from the given type, data and dims.\n *\n * @param type - Specify the element type.\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(type: Tensor.Type, data: Tensor.DataType|readonly number[]|readonly string[]|readonly bigint[]|readonly boolean[],\n dims?: readonly number[]): Tensor;\n\n /**\n * Construct a new tensor object from the given data and dims.\n *\n * @param data - Specify the CPU tensor data.\n * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n */\n new(data: Tensor.DataType, dims?: readonly number[]): Tensor;\n // #endregion\n}\n\n// eslint-disable-next-line @typescript-eslint/naming-convention\nexport const Tensor = TensorImpl as TensorConstructor;\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env} from './env-impl.js';\n\n/**\n * @ignore\n */\nexport const TRACE = (deviceType: string, label: string) => {\n if (typeof env.trace === 'undefined' ? !env.wasm.trace : !env.trace) {\n return;\n }\n // eslint-disable-next-line no-console\n console.timeStamp(`${deviceType}::ORT::${label}`);\n};\n\nconst TRACE_FUNC = (msg: string, extraMsg?: string) => {\n const stack = new Error().stack?.split(/\\r\\n|\\r|\\n/g) || [];\n let hasTraceFunc = false;\n for (let i = 0; i < stack.length; i++) {\n if (hasTraceFunc && !stack[i].includes('TRACE_FUNC')) {\n let label = `FUNC_${msg}::${stack[i].trim().split(' ')[1]}`;\n if (extraMsg) {\n label += `::${extraMsg}`;\n }\n TRACE('CPU', label);\n return;\n }\n if (stack[i].includes('TRACE_FUNC')) {\n hasTraceFunc = true;\n }\n }\n};\n\n/**\n * @ignore\n */\nexport const TRACE_FUNC_BEGIN = (extraMsg?: string) => {\n if (typeof env.trace === 'undefined' ? !env.wasm.trace : !env.trace) {\n return;\n }\n TRACE_FUNC('BEGIN', extraMsg);\n};\n\n/**\n * @ignore\n */\nexport const TRACE_FUNC_END = (extraMsg?: string) => {\n if (typeof env.trace === 'undefined' ? !env.wasm.trace : !env.trace) {\n return;\n }\n TRACE_FUNC('END', extraMsg);\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {resolveBackendAndExecutionProviders} from './backend-impl.js';\nimport {InferenceSessionHandler} from './backend.js';\nimport {InferenceSession as InferenceSessionInterface} from './inference-session.js';\nimport {OnnxValue} from './onnx-value.js';\nimport {Tensor} from './tensor.js';\nimport {TRACE_FUNC_BEGIN, TRACE_FUNC_END} from './trace.js';\n\ntype SessionOptions = InferenceSessionInterface.SessionOptions;\ntype RunOptions = InferenceSessionInterface.RunOptions;\ntype FeedsType = InferenceSessionInterface.FeedsType;\ntype FetchesType = InferenceSessionInterface.FetchesType;\ntype ReturnType = InferenceSessionInterface.ReturnType;\n\nexport class InferenceSession implements InferenceSessionInterface {\n private constructor(handler: InferenceSessionHandler) {\n this.handler = handler;\n }\n run(feeds: FeedsType, options?: RunOptions): Promise;\n run(feeds: FeedsType, fetches: FetchesType, options?: RunOptions): Promise;\n async run(feeds: FeedsType, arg1?: FetchesType|RunOptions, arg2?: RunOptions): Promise {\n TRACE_FUNC_BEGIN();\n const fetches: {[name: string]: OnnxValue|null} = {};\n let options: RunOptions = {};\n // check inputs\n if (typeof feeds !== 'object' || feeds === null || feeds instanceof Tensor || Array.isArray(feeds)) {\n throw new TypeError(\n '\\'feeds\\' must be an object that use input names as keys and OnnxValue as corresponding values.');\n }\n\n let isFetchesEmpty = true;\n // determine which override is being used\n if (typeof arg1 === 'object') {\n if (arg1 === null) {\n throw new TypeError('Unexpected argument[1]: cannot be null.');\n }\n if (arg1 instanceof Tensor) {\n throw new TypeError('\\'fetches\\' cannot be a Tensor');\n }\n\n if (Array.isArray(arg1)) {\n if (arg1.length === 0) {\n throw new TypeError('\\'fetches\\' cannot be an empty array.');\n }\n isFetchesEmpty = false;\n // output names\n for (const name of arg1) {\n if (typeof name !== 'string') {\n throw new TypeError('\\'fetches\\' must be a string array or an object.');\n }\n if (this.outputNames.indexOf(name) === -1) {\n throw new RangeError(`'fetches' contains invalid output name: ${name}.`);\n }\n fetches[name] = null;\n }\n\n if (typeof arg2 === 'object' && arg2 !== null) {\n options = arg2;\n } else if (typeof arg2 !== 'undefined') {\n throw new TypeError('\\'options\\' must be an object.');\n }\n } else {\n // decide whether arg1 is fetches or options\n // if any output name is present and its value is valid OnnxValue, we consider it fetches\n let isFetches = false;\n const arg1Keys = Object.getOwnPropertyNames(arg1);\n for (const name of this.outputNames) {\n if (arg1Keys.indexOf(name) !== -1) {\n const v = (arg1 as InferenceSessionInterface.NullableOnnxValueMapType)[name];\n if (v === null || v instanceof Tensor) {\n isFetches = true;\n isFetchesEmpty = false;\n fetches[name] = v;\n }\n }\n }\n\n if (isFetches) {\n if (typeof arg2 === 'object' && arg2 !== null) {\n options = arg2;\n } else if (typeof arg2 !== 'undefined') {\n throw new TypeError('\\'options\\' must be an object.');\n }\n } else {\n options = arg1 as RunOptions;\n }\n }\n } else if (typeof arg1 !== 'undefined') {\n throw new TypeError('Unexpected argument[1]: must be \\'fetches\\' or \\'options\\'.');\n }\n\n // check if all inputs are in feed\n for (const name of this.inputNames) {\n if (typeof feeds[name] === 'undefined') {\n throw new Error(`input '${name}' is missing in 'feeds'.`);\n }\n }\n\n // if no fetches is specified, we use the full output names list\n if (isFetchesEmpty) {\n for (const name of this.outputNames) {\n fetches[name] = null;\n }\n }\n\n // feeds, fetches and options are prepared\n\n const results = await this.handler.run(feeds, fetches, options);\n const returnValue: {[name: string]: OnnxValue} = {};\n for (const key in results) {\n if (Object.hasOwnProperty.call(results, key)) {\n const result = results[key];\n if (result instanceof Tensor) {\n returnValue[key] = result;\n } else {\n returnValue[key] = new Tensor(result.type, result.data, result.dims);\n }\n }\n }\n TRACE_FUNC_END();\n return returnValue;\n }\n\n async release(): Promise {\n return this.handler.dispose();\n }\n\n static create(path: string, options?: SessionOptions): Promise;\n static create(buffer: ArrayBufferLike, options?: SessionOptions): Promise;\n static create(buffer: ArrayBufferLike, byteOffset: number, byteLength?: number, options?: SessionOptions):\n Promise;\n static create(buffer: Uint8Array, options?: SessionOptions): Promise;\n static async create(\n arg0: string|ArrayBufferLike|Uint8Array, arg1?: SessionOptions|number, arg2?: number,\n arg3?: SessionOptions): Promise {\n TRACE_FUNC_BEGIN();\n // either load from a file or buffer\n let filePathOrUint8Array: string|Uint8Array;\n let options: SessionOptions = {};\n\n if (typeof arg0 === 'string') {\n filePathOrUint8Array = arg0;\n if (typeof arg1 === 'object' && arg1 !== null) {\n options = arg1;\n } else if (typeof arg1 !== 'undefined') {\n throw new TypeError('\\'options\\' must be an object.');\n }\n } else if (arg0 instanceof Uint8Array) {\n filePathOrUint8Array = arg0;\n if (typeof arg1 === 'object' && arg1 !== null) {\n options = arg1;\n } else if (typeof arg1 !== 'undefined') {\n throw new TypeError('\\'options\\' must be an object.');\n }\n } else if (\n arg0 instanceof ArrayBuffer ||\n (typeof SharedArrayBuffer !== 'undefined' && arg0 instanceof SharedArrayBuffer)) {\n const buffer = arg0;\n let byteOffset = 0;\n let byteLength = arg0.byteLength;\n if (typeof arg1 === 'object' && arg1 !== null) {\n options = arg1;\n } else if (typeof arg1 === 'number') {\n byteOffset = arg1;\n if (!Number.isSafeInteger(byteOffset)) {\n throw new RangeError('\\'byteOffset\\' must be an integer.');\n }\n if (byteOffset < 0 || byteOffset >= buffer.byteLength) {\n throw new RangeError(`'byteOffset' is out of range [0, ${buffer.byteLength}).`);\n }\n byteLength = arg0.byteLength - byteOffset;\n if (typeof arg2 === 'number') {\n byteLength = arg2;\n if (!Number.isSafeInteger(byteLength)) {\n throw new RangeError('\\'byteLength\\' must be an integer.');\n }\n if (byteLength <= 0 || byteOffset + byteLength > buffer.byteLength) {\n throw new RangeError(`'byteLength' is out of range (0, ${buffer.byteLength - byteOffset}].`);\n }\n if (typeof arg3 === 'object' && arg3 !== null) {\n options = arg3;\n } else if (typeof arg3 !== 'undefined') {\n throw new TypeError('\\'options\\' must be an object.');\n }\n } else if (typeof arg2 !== 'undefined') {\n throw new TypeError('\\'byteLength\\' must be a number.');\n }\n } else if (typeof arg1 !== 'undefined') {\n throw new TypeError('\\'options\\' must be an object.');\n }\n filePathOrUint8Array = new Uint8Array(buffer, byteOffset, byteLength);\n } else {\n throw new TypeError('Unexpected argument[0]: must be \\'path\\' or \\'buffer\\'.');\n }\n\n // resolve backend, update session options with validated EPs, and create session handler\n const [backend, optionsWithValidatedEPs] = await resolveBackendAndExecutionProviders(options);\n const handler = await backend.createInferenceSessionHandler(filePathOrUint8Array, optionsWithValidatedEPs);\n TRACE_FUNC_END();\n return new InferenceSession(handler);\n }\n\n startProfiling(): void {\n this.handler.startProfiling();\n }\n endProfiling(): void {\n this.handler.endProfiling();\n }\n\n get inputNames(): readonly string[] {\n return this.handler.inputNames;\n }\n get outputNames(): readonly string[] {\n return this.handler.outputNames;\n }\n\n private handler: InferenceSessionHandler;\n}\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession as InferenceSessionImpl} from './inference-session-impl.js';\nimport {OnnxModelOptions} from './onnx-model.js';\nimport {OnnxValue, OnnxValueDataLocation} from './onnx-value.js';\n\n/* eslint-disable @typescript-eslint/no-redeclare */\n\nexport declare namespace InferenceSession {\n // #region input/output types\n\n type OnnxValueMapType = {readonly [name: string]: OnnxValue};\n type NullableOnnxValueMapType = {readonly [name: string]: OnnxValue | null};\n\n /**\n * A feeds (model inputs) is an object that uses input names as keys and OnnxValue as corresponding values.\n */\n type FeedsType = OnnxValueMapType;\n\n /**\n * A fetches (model outputs) could be one of the following:\n *\n * - Omitted. Use model's output names definition.\n * - An array of string indicating the output names.\n * - An object that use output names as keys and OnnxValue or null as corresponding values.\n *\n * @remark\n * different from input argument, in output, OnnxValue is optional. If an OnnxValue is present it will be\n * used as a pre-allocated value by the inference engine; if omitted, inference engine will allocate buffer\n * internally.\n */\n type FetchesType = readonly string[]|NullableOnnxValueMapType;\n\n /**\n * A inferencing return type is an object that uses output names as keys and OnnxValue as corresponding values.\n */\n type ReturnType = OnnxValueMapType;\n\n // #endregion\n\n // #region session options\n\n /**\n * A set of configurations for session behavior.\n */\n export interface SessionOptions extends OnnxModelOptions {\n /**\n * An array of execution provider options.\n *\n * An execution provider option can be a string indicating the name of the execution provider,\n * or an object of corresponding type.\n */\n executionProviders?: readonly ExecutionProviderConfig[];\n\n /**\n * The intra OP threads number.\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native).\n */\n intraOpNumThreads?: number;\n\n /**\n * The inter OP threads number.\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native).\n */\n interOpNumThreads?: number;\n\n /**\n * The free dimension override.\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n freeDimensionOverrides?: {readonly [dimensionName: string]: number};\n\n /**\n * The optimization level.\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n graphOptimizationLevel?: 'disabled'|'basic'|'extended'|'all';\n\n /**\n * Whether enable CPU memory arena.\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n enableCpuMemArena?: boolean;\n\n /**\n * Whether enable memory pattern.\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n enableMemPattern?: boolean;\n\n /**\n * Execution mode.\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n executionMode?: 'sequential'|'parallel';\n\n /**\n * Optimized model file path.\n *\n * If this setting is specified, the optimized model will be dumped. In browser, a blob will be created\n * with a pop-up window.\n */\n optimizedModelFilePath?: string;\n\n /**\n * Whether enable profiling.\n *\n * This setting is a placeholder for a future use.\n */\n enableProfiling?: boolean;\n\n /**\n * File prefix for profiling.\n *\n * This setting is a placeholder for a future use.\n */\n profileFilePrefix?: string;\n\n /**\n * Log ID.\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n logId?: string;\n\n /**\n * Log severity level. See\n * https://github.com/microsoft/onnxruntime/blob/main/include/onnxruntime/core/common/logging/severity.h\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n logSeverityLevel?: 0|1|2|3|4;\n\n /**\n * Log verbosity level.\n *\n * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n */\n logVerbosityLevel?: number;\n\n /**\n * Specify string as a preferred data location for all outputs, or an object that use output names as keys and a\n * preferred data location as corresponding values.\n *\n * This setting is available only in ONNXRuntime Web for WebGL and WebGPU EP.\n */\n preferredOutputLocation?: OnnxValueDataLocation|{readonly [outputName: string]: OnnxValueDataLocation};\n\n /**\n * Whether enable graph capture.\n * This setting is available only in ONNXRuntime Web for WebGPU EP.\n */\n enableGraphCapture?: boolean;\n\n /**\n * Store configurations for a session. See\n * https://github.com/microsoft/onnxruntime/blob/main/include/onnxruntime/core/session/\n * onnxruntime_session_options_config_keys.h\n *\n * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n *\n * @example\n * ```js\n * extra: {\n * session: {\n * set_denormal_as_zero: \"1\",\n * disable_prepacking: \"1\"\n * },\n * optimization: {\n * enable_gelu_approximation: \"1\"\n * }\n * }\n * ```\n */\n extra?: Record;\n }\n\n // #region execution providers\n\n // Currently, we have the following backends to support execution providers:\n // Backend Node.js binding: supports 'cpu', 'dml' (win32), 'coreml' (macOS) and 'cuda' (linux).\n // Backend WebAssembly: supports 'cpu', 'wasm', 'webgpu' and 'webnn'.\n // Backend ONNX.js: supports 'webgl'.\n // Backend React Native: supports 'cpu', 'xnnpack', 'coreml' (iOS), 'nnapi' (Android).\n interface ExecutionProviderOptionMap {\n coreml: CoreMLExecutionProviderOption;\n cpu: CpuExecutionProviderOption;\n cuda: CudaExecutionProviderOption;\n dml: DmlExecutionProviderOption;\n nnapi: NnapiExecutionProviderOption;\n tensorrt: TensorRtExecutionProviderOption;\n wasm: WebAssemblyExecutionProviderOption;\n webgl: WebGLExecutionProviderOption;\n webgpu: WebGpuExecutionProviderOption;\n webnn: WebNNExecutionProviderOption;\n xnnpack: XnnpackExecutionProviderOption;\n }\n\n type ExecutionProviderName = keyof ExecutionProviderOptionMap;\n type ExecutionProviderConfig =\n ExecutionProviderOptionMap[ExecutionProviderName]|ExecutionProviderOption|ExecutionProviderName|string;\n\n export interface ExecutionProviderOption {\n readonly name: string;\n }\n export interface CpuExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'cpu';\n useArena?: boolean;\n }\n export interface CudaExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'cuda';\n deviceId?: number;\n }\n export interface DmlExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'dml';\n deviceId?: number;\n }\n export interface TensorRtExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'tensorrt';\n deviceId?: number;\n }\n export interface WebAssemblyExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'wasm';\n }\n export interface WebGLExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'webgl';\n // TODO: add flags\n }\n export interface XnnpackExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'xnnpack';\n }\n export interface WebGpuExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'webgpu';\n preferredLayout?: 'NCHW'|'NHWC';\n }\n export interface WebNNExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'webnn';\n deviceType?: 'cpu'|'gpu'|'npu';\n numThreads?: number;\n powerPreference?: 'default'|'low-power'|'high-performance';\n }\n export interface CoreMLExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'coreml';\n /**\n * The bit flags for CoreML execution provider.\n *\n * ```\n * COREML_FLAG_USE_CPU_ONLY = 0x001\n * COREML_FLAG_ENABLE_ON_SUBGRAPH = 0x002\n * COREML_FLAG_ONLY_ENABLE_DEVICE_WITH_ANE = 0x004\n * COREML_FLAG_ONLY_ALLOW_STATIC_INPUT_SHAPES = 0x008\n * COREML_FLAG_CREATE_MLPROGRAM = 0x010\n * ```\n *\n * See include/onnxruntime/core/providers/coreml/coreml_provider_factory.h for more details.\n *\n * This flag is available only in ONNXRuntime (Node.js binding).\n */\n coreMlFlags?: number;\n /**\n * Specify whether to use CPU only in CoreML EP.\n *\n * This setting is available only in ONNXRuntime (react-native).\n */\n useCPUOnly?: boolean;\n /**\n * Specify whether to enable CoreML EP on subgraph.\n *\n * This setting is available only in ONNXRuntime (react-native).\n */\n enableOnSubgraph?: boolean;\n /**\n * Specify whether to only enable CoreML EP for Apple devices with ANE (Apple Neural Engine).\n *\n * This setting is available only in ONNXRuntime (react-native).\n */\n onlyEnableDeviceWithANE?: boolean;\n }\n export interface NnapiExecutionProviderOption extends ExecutionProviderOption {\n readonly name: 'nnapi';\n useFP16?: boolean;\n useNCHW?: boolean;\n cpuDisabled?: boolean;\n cpuOnly?: boolean;\n }\n // #endregion\n\n // #endregion\n\n // #region run options\n\n /**\n * A set of configurations for inference run behavior\n */\n export interface RunOptions {\n /**\n * Log severity level. See\n * https://github.com/microsoft/onnxruntime/blob/main/include/onnxruntime/core/common/logging/severity.h\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n logSeverityLevel?: 0|1|2|3|4;\n\n /**\n * Log verbosity level.\n *\n * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n */\n logVerbosityLevel?: number;\n\n /**\n * Terminate all incomplete OrtRun calls as soon as possible if true\n *\n * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n */\n terminate?: boolean;\n\n /**\n * A tag for the Run() calls using this\n *\n * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n */\n tag?: string;\n\n /**\n * Set a single run configuration entry. See\n * https://github.com/microsoft/onnxruntime/blob/main/include/onnxruntime/core/session/\n * onnxruntime_run_options_config_keys.h\n *\n * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n *\n * @example\n *\n * ```js\n * extra: {\n * memory: {\n * enable_memory_arena_shrinkage: \"1\",\n * }\n * }\n * ```\n */\n extra?: Record;\n }\n\n // #endregion\n\n // #region value metadata\n\n // eslint-disable-next-line @typescript-eslint/no-empty-interface\n interface ValueMetadata {\n // TBD\n }\n\n // #endregion\n}\n\n/**\n * Represent a runtime instance of an ONNX model.\n */\nexport interface InferenceSession {\n // #region run()\n\n /**\n * Execute the model asynchronously with the given feeds and options.\n *\n * @param feeds - Representation of the model input. See type description of `InferenceSession.InputType` for detail.\n * @param options - Optional. A set of options that controls the behavior of model inference.\n * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding values.\n */\n run(feeds: InferenceSession.FeedsType, options?: InferenceSession.RunOptions): Promise;\n\n /**\n * Execute the model asynchronously with the given feeds, fetches and options.\n *\n * @param feeds - Representation of the model input. See type description of `InferenceSession.InputType` for detail.\n * @param fetches - Representation of the model output. See type description of `InferenceSession.OutputType` for\n * detail.\n * @param options - Optional. A set of options that controls the behavior of model inference.\n * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding values.\n */\n run(feeds: InferenceSession.FeedsType, fetches: InferenceSession.FetchesType,\n options?: InferenceSession.RunOptions): Promise;\n\n // #endregion\n\n // #region release()\n\n /**\n * Release the inference session and the underlying resources.\n */\n release(): Promise;\n\n // #endregion\n\n // #region profiling\n\n /**\n * Start profiling.\n */\n startProfiling(): void;\n\n /**\n * End profiling.\n */\n endProfiling(): void;\n\n // #endregion\n\n // #region metadata\n\n /**\n * Get input names of the loaded model.\n */\n readonly inputNames: readonly string[];\n\n /**\n * Get output names of the loaded model.\n */\n readonly outputNames: readonly string[];\n\n // /**\n // * Get input metadata of the loaded model.\n // */\n // readonly inputMetadata: ReadonlyArray>;\n\n // /**\n // * Get output metadata of the loaded model.\n // */\n // readonly outputMetadata: ReadonlyArray>;\n\n // #endregion\n}\n\nexport interface InferenceSessionFactory {\n // #region create()\n\n /**\n * Create a new inference session and load model asynchronously from an ONNX model file.\n *\n * @param uri - The URI or file path of the model to load.\n * @param options - specify configuration for creating a new inference session.\n * @returns A promise that resolves to an InferenceSession object.\n */\n create(uri: string, options?: InferenceSession.SessionOptions): Promise;\n\n /**\n * Create a new inference session and load model asynchronously from an array bufer.\n *\n * @param buffer - An ArrayBuffer representation of an ONNX model.\n * @param options - specify configuration for creating a new inference session.\n * @returns A promise that resolves to an InferenceSession object.\n */\n create(buffer: ArrayBufferLike, options?: InferenceSession.SessionOptions): Promise;\n\n /**\n * Create a new inference session and load model asynchronously from segment of an array bufer.\n *\n * @param buffer - An ArrayBuffer representation of an ONNX model.\n * @param byteOffset - The beginning of the specified portion of the array buffer.\n * @param byteLength - The length in bytes of the array buffer.\n * @param options - specify configuration for creating a new inference session.\n * @returns A promise that resolves to an InferenceSession object.\n */\n create(buffer: ArrayBufferLike, byteOffset: number, byteLength?: number, options?: InferenceSession.SessionOptions):\n Promise;\n\n /**\n * Create a new inference session and load model asynchronously from a Uint8Array.\n *\n * @param buffer - A Uint8Array representation of an ONNX model.\n * @param options - specify configuration for creating a new inference session.\n * @returns A promise that resolves to an InferenceSession object.\n */\n create(buffer: Uint8Array, options?: InferenceSession.SessionOptions): Promise;\n\n // #endregion\n}\n\n// eslint-disable-next-line @typescript-eslint/naming-convention\nexport const InferenceSession: InferenceSessionFactory = InferenceSessionImpl;\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {OptionsFormat, OptionsNormalizationParameters, OptionsTensorLayout} from './tensor-factory.js';\n\nexport interface TensorToDataUrlOptions extends OptionsTensorLayout, OptionsFormat, OptionsNormalizationParameters {}\n\nexport interface TensorToImageDataOptions extends OptionsTensorLayout, OptionsFormat, OptionsNormalizationParameters {}\n\nexport interface ConversionUtils {\n /**\n * creates a DataURL instance from tensor\n *\n * @param options - An optional object representing options for creating a DataURL instance from the tensor.\n *\n * The following default settings will be applied:\n * - `format`: `'RGB'`\n * - `tensorLayout`: `'NCHW'`\n * @returns a DataURL string representing the image converted from tensor data\n */\n toDataURL(options?: TensorToDataUrlOptions): string;\n\n /**\n * creates an ImageData instance from tensor\n *\n * @param options - An optional object representing options for creating an ImageData instance from the tensor.\n *\n * The following default settings will be applied:\n * - `format`: `'RGB'`\n * - `tensorLayout`: `'NCHW'`\n * @returns an ImageData instance representing the image converted from tensor data\n */\n toImageData(options?: TensorToImageDataOptions): ImageData;\n}\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor, TypedTensor} from './tensor.js';\n\nexport type ImageFormat = 'RGB'|'RGBA'|'BGR'|'RBG';\nexport type ImageTensorLayout = 'NHWC'|'NCHW';\n\n// the following region contains type definitions for constructing tensor from a specific location.\n\n// #region types for constructing a tensor from a specific location\n\n/**\n * represent common properties of the parameter for constructing a tensor from a specific location.\n */\ninterface CommonConstructorParameters extends Pick {\n /**\n * Specify the data type of the tensor.\n */\n readonly type: T;\n}\n\n/**\n * represent the parameter for constructing a tensor from a GPU resource.\n */\ninterface GpuResourceConstructorParameters {\n /**\n * an optional callback function to download data from GPU to CPU.\n *\n * If not provided, the tensor treat the GPU data as external resource.\n */\n download?(): Promise;\n\n /**\n * an optional callback function that will be called when the tensor is disposed.\n *\n * If not provided, the tensor treat the GPU data as external resource.\n */\n dispose?(): void;\n}\n\n/**\n * represent the parameter for constructing a tensor from a pinned CPU buffer\n */\nexport interface CpuPinnedConstructorParameters extends\n CommonConstructorParameters {\n /**\n * Specify the location of the data to be 'cpu-pinned'.\n */\n readonly location: 'cpu-pinned';\n /**\n * Specify the CPU pinned buffer that holds the tensor data.\n */\n readonly data: Tensor.DataTypeMap[T];\n}\n\n/**\n * represent the parameter for constructing a tensor from a WebGL texture\n */\nexport interface TextureConstructorParameters extends\n CommonConstructorParameters, GpuResourceConstructorParameters {\n /**\n * Specify the location of the data to be 'texture'.\n */\n readonly location: 'texture';\n /**\n * Specify the WebGL texture that holds the tensor data.\n */\n readonly texture: Tensor.TextureType;\n}\n\n/**\n * represent the parameter for constructing a tensor from a WebGPU buffer\n */\nexport interface GpuBufferConstructorParameters extends\n CommonConstructorParameters, GpuResourceConstructorParameters {\n /**\n * Specify the location of the data to be 'gpu-buffer'.\n */\n readonly location: 'gpu-buffer';\n /**\n * Specify the WebGPU buffer that holds the tensor data.\n */\n readonly gpuBuffer: Tensor.GpuBufferType;\n}\n\n// #endregion\n\n// the following region contains type definitions of each individual options.\n// the tensor factory functions use a composition of those options as the parameter type.\n\n// #region Options fields\n\nexport interface OptionsFormat {\n /**\n * Describes the image format represented in RGBA color space.\n */\n format?: ImageFormat;\n}\n\nexport interface OptionsTensorFormat {\n /**\n * Describes the image format of the tensor.\n *\n * NOTE: this is different from option 'format'. While option 'format' represents the original image, 'tensorFormat'\n * represents the target format of the tensor. A transpose will be performed if they are different.\n */\n tensorFormat?: ImageFormat;\n}\n\nexport interface OptionsTensorDataType {\n /**\n * Describes the data type of the tensor.\n */\n dataType?: 'float32'|'uint8';\n}\n\nexport interface OptionsTensorLayout {\n /**\n * Describes the tensor layout when representing data of one or more image(s).\n */\n tensorLayout?: ImageTensorLayout;\n}\n\nexport interface OptionsDimensions {\n /**\n * Describes the image height in pixel\n */\n height?: number;\n /**\n * Describes the image width in pixel\n */\n width?: number;\n}\n\nexport interface OptionResizedDimensions {\n /**\n * Describes the resized height. If omitted, original height will be used.\n */\n resizedHeight?: number;\n /**\n * Describes resized width - can be accessed via tensor dimensions as well\n */\n resizedWidth?: number;\n}\n\nexport interface OptionsNormalizationParameters {\n /**\n * Describes normalization parameters when preprocessing the image as model input.\n *\n * Data element are ranged from 0 to 255.\n */\n norm?: {\n /**\n * The 'bias' value for image normalization.\n * - If omitted, use default value 0.\n * - If it's a single number, apply to each channel\n * - If it's an array of 3 or 4 numbers, apply element-wise. Number of elements need to match the number of channels\n * for the corresponding image format\n */\n bias?: number|[number, number, number]|[number, number, number, number];\n /**\n * The 'mean' value for image normalization.\n * - If omitted, use default value 255.\n * - If it's a single number, apply to each channel\n * - If it's an array of 3 or 4 numbers, apply element-wise. Number of elements need to match the number of channels\n * for the corresponding image format\n */\n mean?: number | [number, number, number] | [number, number, number, number];\n };\n}\n\n// #endregion\n\n// #region Options composition\n\nexport interface TensorFromImageDataOptions extends OptionResizedDimensions, OptionsTensorFormat, OptionsTensorLayout,\n OptionsTensorDataType, OptionsNormalizationParameters {}\n\nexport interface TensorFromImageElementOptions extends OptionResizedDimensions, OptionsTensorFormat,\n OptionsTensorLayout, OptionsTensorDataType,\n OptionsNormalizationParameters {}\n\nexport interface TensorFromUrlOptions extends OptionsDimensions, OptionResizedDimensions, OptionsTensorFormat,\n OptionsTensorLayout, OptionsTensorDataType,\n OptionsNormalizationParameters {}\n\nexport interface TensorFromImageBitmapOptions extends OptionResizedDimensions, OptionsTensorFormat, OptionsTensorLayout,\n OptionsTensorDataType, OptionsNormalizationParameters {}\n\nexport interface TensorFromTextureOptions extends\n Required, OptionsFormat, GpuResourceConstructorParameters/* TODO: add more */ {}\n\nexport interface TensorFromGpuBufferOptions extends\n Pick, GpuResourceConstructorParameters {\n /**\n * Describes the data type of the tensor.\n */\n dataType?: T;\n}\n\n// #endregion\n\n/**\n * type TensorFactory defines the factory functions of 'Tensor' to create tensor instances from existing data or\n * resources.\n */\nexport interface TensorFactory {\n /**\n * create a tensor from an ImageData object\n *\n * @param imageData - the ImageData object to create tensor from\n * @param options - An optional object representing options for creating tensor from ImageData.\n *\n * The following default settings will be applied:\n * - `tensorFormat`: `'RGB'`\n * - `tensorLayout`: `'NCHW'`\n * - `dataType`: `'float32'`\n * @returns A promise that resolves to a tensor object\n */\n fromImage(imageData: ImageData, options?: TensorFromImageDataOptions):\n Promise|TypedTensor<'uint8'>>;\n\n /**\n * create a tensor from a HTMLImageElement object\n *\n * @param imageElement - the HTMLImageElement object to create tensor from\n * @param options - An optional object representing options for creating tensor from HTMLImageElement.\n *\n * The following default settings will be applied:\n * - `tensorFormat`: `'RGB'`\n * - `tensorLayout`: `'NCHW'`\n * - `dataType`: `'float32'`\n * @returns A promise that resolves to a tensor object\n */\n fromImage(imageElement: HTMLImageElement, options?: TensorFromImageElementOptions):\n Promise|TypedTensor<'uint8'>>;\n\n /**\n * create a tensor from URL\n *\n * @param urlSource - a string as a URL to the image or a data URL containing the image data.\n * @param options - An optional object representing options for creating tensor from URL.\n *\n * The following default settings will be applied:\n * - `tensorFormat`: `'RGB'`\n * - `tensorLayout`: `'NCHW'`\n * - `dataType`: `'float32'`\n * @returns A promise that resolves to a tensor object\n */\n fromImage(urlSource: string, options?: TensorFromUrlOptions): Promise|TypedTensor<'uint8'>>;\n\n /**\n * create a tensor from an ImageBitmap object\n *\n * @param bitmap - the ImageBitmap object to create tensor from\n * @param options - An optional object representing options for creating tensor from URL.\n *\n * The following default settings will be applied:\n * - `tensorFormat`: `'RGB'`\n * - `tensorLayout`: `'NCHW'`\n * - `dataType`: `'float32'`\n * @returns A promise that resolves to a tensor object\n */\n fromImage(bitmap: ImageBitmap, options: TensorFromImageBitmapOptions):\n Promise|TypedTensor<'uint8'>>;\n\n /**\n * create a tensor from a WebGL texture\n *\n * @param texture - the WebGLTexture object to create tensor from\n * @param options - An optional object representing options for creating tensor from WebGL texture.\n *\n * The options include following properties:\n * - `width`: the width of the texture. Required.\n * - `height`: the height of the texture. Required.\n * - `format`: the format of the texture. If omitted, assume 'RGBA'.\n * - `download`: an optional function to download the tensor data from GPU to CPU. If omitted, the GPU data\n * will not be able to download. Usually, this is provided by a GPU backend for the inference outputs. Users don't\n * need to provide this function.\n * - `dispose`: an optional function to dispose the tensor data on GPU. If omitted, the GPU data will not be disposed.\n * Usually, this is provided by a GPU backend for the inference outputs. Users don't need to provide this function.\n *\n * @returns a tensor object\n */\n fromTexture(\n texture: Tensor.TextureType, options: TensorFromTextureOptions): TypedTensor<'float32'>;\n\n /**\n * create a tensor from a WebGPU buffer\n *\n * @param buffer - the GPUBuffer object to create tensor from\n * @param options - An optional object representing options for creating tensor from WebGPU buffer.\n *\n * The options include following properties:\n * - `dataType`: the data type of the tensor. If omitted, assume 'float32'.\n * - `dims`: the dimension of the tensor. Required.\n * - `download`: an optional function to download the tensor data from GPU to CPU. If omitted, the GPU data\n * will not be able to download. Usually, this is provided by a GPU backend for the inference outputs. Users don't\n * need to provide this function.\n * - `dispose`: an optional function to dispose the tensor data on GPU. If omitted, the GPU data will not be disposed.\n * Usually, this is provided by a GPU backend for the inference outputs. Users don't need to provide this function.\n *\n * @returns a tensor object\n */\n fromGpuBuffer(\n buffer: Tensor.GpuBufferType, options: TensorFromGpuBufferOptions): TypedTensor;\n\n /**\n * create a tensor from a pre-allocated buffer. The buffer will be used as a pinned buffer.\n *\n * @param type - the tensor element type.\n * @param buffer - a TypedArray corresponding to the type.\n * @param dims - specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n *\n * @returns a tensor object\n */\n fromPinnedBuffer>(\n type: T, buffer: Tensor.DataTypeMap[T], dims?: readonly number[]): TypedTensor;\n}\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/**\n * A string that represents a file's URL or path.\n *\n * Path is vailable only in onnxruntime-node or onnxruntime-web running in Node.js.\n */\nexport type FileUrlOrPath = string;\n\n/**\n * A Blob object that represents a file.\n */\nexport type FileBlob = Blob;\n\n/**\n * A Uint8Array, ArrayBuffer or SharedArrayBuffer object that represents a file content.\n *\n * When it is an ArrayBuffer or SharedArrayBuffer, the whole buffer is assumed to be the file content.\n */\nexport type FileData = Uint8Array|ArrayBufferLike;\n\n/**\n * Represents a file that can be loaded by the ONNX Runtime JavaScript API.\n */\nexport type FileType = FileUrlOrPath|FileBlob|FileData;\n\n/**\n * Represents an external data file.\n */\nexport interface ExternalDataFileDescription {\n /**\n * Specify the external data file.\n */\n data: FileType;\n /**\n * Specify the file path.\n */\n path: string;\n}\n\n/**\n * Represents an external data file.\n *\n * When using a string, it should be a file URL or path that in the same directory as the model file.\n */\nexport type ExternalDataFileType = ExternalDataFileDescription|FileUrlOrPath;\n\n/**\n * Options for model loading.\n */\nexport interface OnnxModelOptions {\n /**\n * Specifying a list of files that represents the external data.\n */\n externalData?: readonly ExternalDataFileType[];\n}\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from './tensor.js';\n\nexport type NonTensorType = never;\n\n/**\n * Type OnnxValue Represents both tensors and non-tensors value for model's inputs/outputs.\n *\n * NOTE: currently not support non-tensor\n */\nexport type OnnxValue = Tensor|NonTensorType;\n\n/**\n * Type OnnxValueDataLocation represents the location of the data of an OnnxValue.\n */\nexport type OnnxValueDataLocation = Tensor.DataLocation;\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {resolveBackendAndExecutionProviders} from './backend-impl.js';\nimport {SessionHandler, TrainingSessionHandler} from './backend.js';\nimport {InferenceSession as InferenceSession} from './inference-session.js';\nimport {OnnxValue} from './onnx-value.js';\nimport {Tensor} from './tensor.js';\nimport {TrainingSession as TrainingSessionInterface, TrainingSessionCreateOptions} from './training-session.js';\n\ntype SessionOptions = InferenceSession.SessionOptions;\ntype FeedsType = InferenceSession.FeedsType;\ntype FetchesType = InferenceSession.FetchesType;\ntype ReturnType = InferenceSession.ReturnType;\ntype RunOptions = InferenceSession.RunOptions;\n\nconst noBackendErrMsg: string = 'Training backend could not be resolved. ' +\n 'Make sure you\\'re using the correct configuration & WebAssembly files.';\n\nexport class TrainingSession implements TrainingSessionInterface {\n private constructor(handler: TrainingSessionHandler, hasOptimizerModel: boolean, hasEvalModel: boolean) {\n this.handler = handler;\n this.hasOptimizerModel = hasOptimizerModel;\n this.hasEvalModel = hasEvalModel;\n }\n private handler: TrainingSessionHandler;\n private hasOptimizerModel: boolean;\n private hasEvalModel: boolean;\n\n get trainingInputNames(): readonly string[] {\n return this.handler.inputNames;\n }\n get trainingOutputNames(): readonly string[] {\n return this.handler.outputNames;\n }\n\n get evalInputNames(): readonly string[] {\n if (this.hasEvalModel) {\n return this.handler.evalInputNames;\n } else {\n throw new Error('This training session has no evalModel loaded.');\n }\n }\n get evalOutputNames(): readonly string[] {\n if (this.hasEvalModel) {\n return this.handler.evalOutputNames;\n } else {\n throw new Error('This training session has no evalModel loaded.');\n }\n }\n\n static async create(trainingOptions: TrainingSessionCreateOptions, sessionOptions?: SessionOptions):\n Promise {\n const evalModel: string|Uint8Array = trainingOptions.evalModel || '';\n const optimizerModel: string|Uint8Array = trainingOptions.optimizerModel || '';\n const options: SessionOptions = sessionOptions || {};\n\n // resolve backend, update session options with validated EPs, and create session handler\n const [backend, optionsWithValidatedEPs] = await resolveBackendAndExecutionProviders(options);\n if (backend.createTrainingSessionHandler) {\n const handler = await backend.createTrainingSessionHandler(\n trainingOptions.checkpointState, trainingOptions.trainModel, evalModel, optimizerModel,\n optionsWithValidatedEPs);\n return new TrainingSession(handler, !!trainingOptions.optimizerModel, !!trainingOptions.evalModel);\n } else {\n throw new Error(noBackendErrMsg);\n }\n }\n\n /**\n * Helper function for runTrainStep and future runStep methods that handles the type-narrowing conversion from\n * the given parameters to SessionHandler.FetchesType and RunOptions.\n *\n * @param inputNames the feeds object is checked that they contain all input names in the provided list of input\n * names.\n * @param outputNames the fetches object is checked that their keys match up with valid names in the list of output\n * names.\n * @param feeds the required input\n * @param arg1 narrowed & converted into the SessionHandler.FetchesType or RunOptions object\n * @param arg2 optional RunOptions object.\n * @returns\n */\n typeNarrowingForRunStep(\n inputNames: readonly string[], outputNames: readonly string[], feeds: FeedsType, arg1?: FetchesType|RunOptions,\n arg2?: RunOptions): [SessionHandler.FetchesType, RunOptions] {\n const fetches: {[name: string]: OnnxValue|null} = {};\n let options: RunOptions = {};\n // check inputs\n if (typeof feeds !== 'object' || feeds === null || feeds instanceof Tensor || Array.isArray(feeds)) {\n throw new TypeError(\n '\\'feeds\\' must be an object that use input names as keys and OnnxValue as corresponding values.');\n }\n\n let isFetchesEmpty = true;\n // determine which override is being used\n if (typeof arg1 === 'object') {\n if (arg1 === null) {\n throw new TypeError('Unexpected argument[1]: cannot be null.');\n }\n if (arg1 instanceof Tensor) {\n throw new TypeError('\\'fetches\\' cannot be a Tensor');\n }\n\n if (Array.isArray(arg1)) {\n if (arg1.length === 0) {\n throw new TypeError('\\'fetches\\' cannot be an empty array.');\n }\n isFetchesEmpty = false;\n // output names\n for (const name of arg1) {\n if (typeof name !== 'string') {\n throw new TypeError('\\'fetches\\' must be a string array or an object.');\n }\n if (outputNames.indexOf(name) === -1) {\n throw new RangeError(`'fetches' contains invalid output name: ${name}.`);\n }\n fetches[name] = null;\n }\n\n if (typeof arg2 === 'object' && arg2 !== null) {\n options = arg2;\n } else if (typeof arg2 !== 'undefined') {\n throw new TypeError('\\'options\\' must be an object.');\n }\n } else {\n // decide whether arg1 is fetches or options\n // if any output name is present and its value is valid OnnxValue, we consider it fetches\n let isFetches = false;\n const arg1Keys = Object.getOwnPropertyNames(arg1);\n for (const name of outputNames) {\n if (arg1Keys.indexOf(name) !== -1) {\n const v = (arg1 as InferenceSession.NullableOnnxValueMapType)[name];\n if (v === null || v instanceof Tensor) {\n isFetches = true;\n isFetchesEmpty = false;\n fetches[name] = v;\n }\n }\n }\n\n if (isFetches) {\n if (typeof arg2 === 'object' && arg2 !== null) {\n options = arg2;\n } else if (typeof arg2 !== 'undefined') {\n throw new TypeError('\\'options\\' must be an object.');\n }\n } else {\n options = arg1 as RunOptions;\n }\n }\n } else if (typeof arg1 !== 'undefined') {\n throw new TypeError('Unexpected argument[1]: must be \\'fetches\\' or \\'options\\'.');\n }\n\n // check if all inputs are in feed\n for (const name of inputNames) {\n if (typeof feeds[name] === 'undefined') {\n throw new Error(`input '${name}' is missing in 'feeds'.`);\n }\n }\n\n // if no fetches is specified, we use the full output names list\n if (isFetchesEmpty) {\n for (const name of outputNames) {\n fetches[name] = null;\n }\n }\n\n return [fetches, options];\n }\n\n /**\n * Helper method for runTrainStep and any other runStep methods. Takes the ReturnType result from the SessionHandler\n * and changes it into a map of Tensors.\n *\n * @param results\n * @returns\n */\n convertHandlerReturnTypeToMapOfTensors(results: SessionHandler.ReturnType): ReturnType {\n const returnValue: {[name: string]: OnnxValue} = {};\n for (const key in results) {\n if (Object.hasOwnProperty.call(results, key)) {\n const result = results[key];\n if (result instanceof Tensor) {\n returnValue[key] = result;\n } else {\n returnValue[key] = new Tensor(result.type, result.data, result.dims);\n }\n }\n }\n return returnValue;\n }\n\n async lazyResetGrad(): Promise {\n await this.handler.lazyResetGrad();\n }\n\n runTrainStep(feeds: FeedsType, options?: RunOptions): Promise;\n runTrainStep(feeds: FeedsType, fetches: FetchesType, options?: RunOptions): Promise;\n async runTrainStep(feeds: FeedsType, arg1?: FetchesType|RunOptions, arg2?: RunOptions): Promise {\n const [fetches, options] =\n this.typeNarrowingForRunStep(this.trainingInputNames, this.trainingOutputNames, feeds, arg1, arg2);\n const results = await this.handler.runTrainStep(feeds, fetches, options);\n return this.convertHandlerReturnTypeToMapOfTensors(results);\n }\n\n async runOptimizerStep(options?: InferenceSession.RunOptions|undefined): Promise {\n if (this.hasOptimizerModel) {\n await this.handler.runOptimizerStep(options || {});\n } else {\n throw new Error('This TrainingSession has no OptimizerModel loaded.');\n }\n }\n\n runEvalStep(feeds: FeedsType, options?: RunOptions|undefined): Promise;\n runEvalStep(feeds: FeedsType, fetches: FetchesType, options?: RunOptions|undefined): Promise;\n async runEvalStep(feeds: FeedsType, arg1?: FetchesType|RunOptions, arg2?: RunOptions): Promise {\n if (this.hasEvalModel) {\n const [fetches, options] =\n this.typeNarrowingForRunStep(this.evalInputNames, this.evalOutputNames, feeds, arg1, arg2);\n const results = await this.handler.runEvalStep(feeds, fetches, options);\n return this.convertHandlerReturnTypeToMapOfTensors(results);\n } else {\n throw new Error('This TrainingSession has no EvalModel loaded.');\n }\n }\n\n async getParametersSize(trainableOnly = true): Promise {\n return this.handler.getParametersSize(trainableOnly);\n }\n\n async loadParametersBuffer(array: Uint8Array, trainableOnly = true): Promise {\n const paramsSize = await this.getParametersSize(trainableOnly);\n // checking that the size of the Uint8Array is equivalent to the byte length of a Float32Array of the number\n // of parameters\n if (array.length !== 4 * paramsSize) {\n throw new Error(\n 'Size of the buffer passed into loadParametersBuffer must match the number of parameters in ' +\n 'the model. Please use getParametersSize method to check.');\n }\n return this.handler.loadParametersBuffer(array, trainableOnly);\n }\n\n async getContiguousParameters(trainableOnly = true): Promise {\n return this.handler.getContiguousParameters(trainableOnly);\n }\n\n async release(): Promise {\n return this.handler.dispose();\n }\n}\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession} from './inference-session.js';\nimport {OnnxValue} from './onnx-value.js';\nimport {TrainingSession as TrainingSessionImpl} from './training-session-impl.js';\n\n/* eslint-disable @typescript-eslint/no-redeclare */\n\nexport declare namespace TrainingSession {\n /**\n * Either URI file path (string) or Uint8Array containing model or checkpoint information.\n */\n type UriOrBuffer = string|Uint8Array;\n}\n\n/**\n * Represent a runtime instance of an ONNX training session,\n * which contains a model that can be trained, and, optionally,\n * an eval and optimizer model.\n */\nexport interface TrainingSession {\n // #region run()\n\n /**\n * Lazily resets the gradients of all trainable parameters to zero. Should happen after the invocation of\n * runOptimizerStep.\n */\n lazyResetGrad(): Promise;\n\n /**\n * Run TrainStep asynchronously with the given feeds and options.\n *\n * @param feeds - Representation of the model input. See type description of `InferenceSession.InputType` for\n detail.\n * @param options - Optional. A set of options that controls the behavior of model training.\n * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding values.\n */\n runTrainStep(feeds: InferenceSession.FeedsType, options?: InferenceSession.RunOptions):\n Promise;\n\n /**\n * Run a single train step with the given inputs and options.\n *\n * @param feeds - Representation of the model input.\n * @param fetches - Representation of the model output.\n * detail.\n * @param options - Optional. A set of options that controls the behavior of model training.\n * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding\n values.\n */\n runTrainStep(\n feeds: InferenceSession.FeedsType, fetches: InferenceSession.FetchesType,\n options?: InferenceSession.RunOptions): Promise;\n\n /**\n * Runs a single optimizer step, which performs weight updates for the trainable parameters using the optimizer model.\n *\n * @param options - Optional. A set of options that controls the behavior of model optimizing.\n */\n runOptimizerStep(options?: InferenceSession.RunOptions): Promise;\n\n /**\n * Run a single eval step with the given inputs and options using the eval model.\n *\n * @param feeds - Representation of the model input.\n * @param options - Optional. A set of options that controls the behavior of model eval step.\n * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding\n values.\n */\n runEvalStep(feeds: InferenceSession.FeedsType, options?: InferenceSession.RunOptions):\n Promise;\n\n /**\n * Run a single eval step with the given inputs and options using the eval model.\n *\n * @param feeds - Representation of the model input.\n * @param fetches - Representation of the model output.\n * detail.\n * @param options - Optional. A set of options that controls the behavior of model eval step.\n * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding\n values.\n */\n runEvalStep(\n feeds: InferenceSession.FeedsType, fetches: InferenceSession.FetchesType,\n options?: InferenceSession.RunOptions): Promise;\n\n // #endregion\n\n // #region copy parameters\n\n /**\n * Retrieves the size of all parameters for the training state. Calculates the total number of primitive (datatype of\n * the parameters) elements of all the parameters in the training state.\n *\n * @param trainableOnly - When set to true, the size is calculated for trainable params only. Default value is true.\n */\n getParametersSize(trainableOnly: boolean): Promise;\n\n /**\n * Copies parameter values from the given buffer to the training state. Currently, only supporting models with\n * parameters of type Float32.\n *\n * @param buffer - A Uint8Array representation of Float32 parameters.\n * @param trainableOnly - True if trainable parameters only to be modified, false otherwise. Default value is true.\n */\n loadParametersBuffer(buffer: Uint8Array, trainableOnly: boolean): Promise;\n\n /**\n * Copies the model parameters to a contiguous buffer. Usually used in the context of Federated Learning.\n * Currently, only supporting models with parameters of type Float32.\n *\n * @param trainableOnly - When set to true, only trainable parameters are copied. Trainable parameters are parameters\n * for which requires_grad is set to true. Default value is true.\n * @returns A promise that resolves to a Float32 OnnxValue of the requested parameters.\n */\n getContiguousParameters(trainableOnly: boolean): Promise;\n // #endregion\n\n // #region release()\n\n /**\n * Release the inference session and the underlying resources.\n */\n release(): Promise;\n // #endregion\n\n // #region metadata\n\n /**\n * Get input names of the loaded training model.\n */\n readonly trainingInputNames: readonly string[];\n\n /**\n * Get output names of the loaded training model.\n */\n readonly trainingOutputNames: readonly string[];\n\n /**\n * Get input names of the loaded eval model. Is an empty array if no eval model is loaded.\n */\n readonly evalInputNames: readonly string[];\n\n /**\n * Get output names of the loaded eval model. Is an empty array if no eval model is loaded.\n */\n readonly evalOutputNames: readonly string[];\n\n // #endregion\n}\n\n/**\n * Represents the optional parameters that can be passed into the TrainingSessionFactory.\n */\nexport interface TrainingSessionCreateOptions {\n /**\n * URI or buffer for a .ckpt file that contains the checkpoint for the training model.\n */\n checkpointState: TrainingSession.UriOrBuffer;\n /**\n * URI or buffer for the .onnx training file.\n */\n trainModel: TrainingSession.UriOrBuffer;\n /**\n * Optional. URI or buffer for the .onnx optimizer model file.\n */\n optimizerModel?: TrainingSession.UriOrBuffer;\n /**\n * Optional. URI or buffer for the .onnx eval model file.\n */\n evalModel?: TrainingSession.UriOrBuffer;\n}\n\n/**\n * Defines method overload possibilities for creating a TrainingSession.\n */\nexport interface TrainingSessionFactory {\n // #region create()\n\n /**\n * Creates a new TrainingSession and asynchronously loads any models passed in through trainingOptions\n *\n * @param trainingOptions specify models and checkpoints to load into the Training Session\n * @param sessionOptions specify configuration for training session behavior\n *\n * @returns Promise that resolves to a TrainingSession object\n */\n create(trainingOptions: TrainingSessionCreateOptions, sessionOptions?: InferenceSession.SessionOptions):\n Promise;\n\n // #endregion\n}\n\n// eslint-disable-next-line @typescript-eslint/naming-convention\nexport const TrainingSession: TrainingSessionFactory = TrainingSessionImpl;\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/**\n * # ONNX Runtime JavaScript API\n *\n * ONNX Runtime JavaScript API is a unified API for all JavaScript usages, including the following NPM packages:\n *\n * - [onnxruntime-node](https://www.npmjs.com/package/onnxruntime-node)\n * - [onnxruntime-web](https://www.npmjs.com/package/onnxruntime-web)\n * - [onnxruntime-react-native](https://www.npmjs.com/package/onnxruntime-react-native)\n *\n * See also:\n * - [Get Started](https://onnxruntime.ai/docs/get-started/with-javascript/)\n * - [Inference examples](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/js)\n *\n * @packageDocumentation\n */\n\nexport * from './backend.js';\nexport * from './env.js';\nexport * from './inference-session.js';\nexport * from './tensor.js';\nexport * from './tensor-conversion.js';\nexport * from './tensor-factory.js';\nexport * from './trace.js';\nexport * from './onnx-model.js';\nexport * from './onnx-value.js';\nexport * from './training-session.js';\n", "export const readFile = undefined;export const readFileSync = undefined;export const createReadStream = undefined;", "export const join = undefined;", "\nvar ortWasm = (() => {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(moduleArg = {}) {\n\nvar e=moduleArg,k,l;e.ready=new Promise((a,b)=>{k=a;l=b});var q=Object.assign({},e),v=\"./this.program\",aa=\"object\"==typeof window,x=\"function\"==typeof importScripts,ba=\"object\"==typeof process&&\"object\"==typeof process.versions&&\"string\"==typeof process.versions.node,y=\"\",A,B,C;\nif(ba){var fs=require(\"fs\"),D=require(\"path\");y=x?D.dirname(y)+\"/\":__dirname+\"/\";A=(a,b)=>{a=a.startsWith(\"file://\")?new URL(a):D.normalize(a);return fs.readFileSync(a,b?void 0:\"utf8\")};C=a=>{a=A(a,!0);a.buffer||(a=new Uint8Array(a));return a};B=(a,b,c,f=!0)=>{a=a.startsWith(\"file://\")?new URL(a):D.normalize(a);fs.readFile(a,f?void 0:\"utf8\",(g,h)=>{g?c(g):b(f?h.buffer:h)})};!e.thisProgram&&1\"[Emscripten Module object]\"}else if(aa||\nx)x?y=self.location.href:\"undefined\"!=typeof document&&document.currentScript&&(y=document.currentScript.src),_scriptDir&&(y=_scriptDir),0!==y.indexOf(\"blob:\")?y=y.substr(0,y.replace(/[?#].*/,\"\").lastIndexOf(\"/\")+1):y=\"\",A=a=>{var b=new XMLHttpRequest;b.open(\"GET\",a,!1);b.send(null);return b.responseText},x&&(C=a=>{var b=new XMLHttpRequest;b.open(\"GET\",a,!1);b.responseType=\"arraybuffer\";b.send(null);return new Uint8Array(b.response)}),B=(a,b,c)=>{var f=new XMLHttpRequest;f.open(\"GET\",a,!0);f.responseType=\n\"arraybuffer\";f.onload=()=>{200==f.status||0==f.status&&f.response?b(f.response):c()};f.onerror=c;f.send(null)};var ca=e.print||console.log.bind(console),E=e.printErr||console.error.bind(console);Object.assign(e,q);q=null;e.thisProgram&&(v=e.thisProgram);var F;e.wasmBinary&&(F=e.wasmBinary);var noExitRuntime=e.noExitRuntime||!0;\"object\"!=typeof WebAssembly&&G(\"no native wasm support detected\");var H,I,da=!1,J,K,L,M;\nfunction ea(){var a=H.buffer;e.HEAP8=J=new Int8Array(a);e.HEAP16=new Int16Array(a);e.HEAP32=L=new Int32Array(a);e.HEAPU8=K=new Uint8Array(a);e.HEAPU16=new Uint16Array(a);e.HEAPU32=M=new Uint32Array(a);e.HEAPF32=new Float32Array(a);e.HEAPF64=new Float64Array(a)}var fa=[],ha=[],ia=[];function ja(){var a=e.preRun.shift();fa.unshift(a)}var N=0,O=null,P=null;\nfunction G(a){if(e.onAbort)e.onAbort(a);a=\"Aborted(\"+a+\")\";E(a);da=!0;a=new WebAssembly.RuntimeError(a+\". 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' +\n 'Please consider using onnxruntime-node for performance critical scenarios.');\n }\n\n try {\n // Test for transferability of SABs (for browsers. needed for Firefox)\n // https://groups.google.com/forum/#!msg/mozilla.dev.platform/IHkBZlHETpA/dwsMNchWEQAJ\n if (typeof MessageChannel !== 'undefined') {\n new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));\n }\n\n // Test for WebAssembly threads capability (for both browsers and Node.js)\n // This typed array is a WebAssembly program containing threaded instructions.\n return WebAssembly.validate(new Uint8Array([\n 0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5,\n 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11\n ]));\n } catch (e) {\n return false;\n }\n};\n\nconst isSimdSupported = (): boolean => {\n try {\n // Test for WebAssembly SIMD capability (for both browsers and Node.js)\n // This typed array is a WebAssembly program containing SIMD instructions.\n\n // The binary data is generated from the following code by wat2wasm:\n //\n // (module\n // (type $t0 (func))\n // (func $f0 (type $t0)\n // (drop\n // (i32x4.dot_i16x8_s\n // (i8x16.splat\n // (i32.const 0))\n // (v128.const i32x4 0x00000000 0x00000000 0x00000000 0x00000000)))))\n\n return WebAssembly.validate(new Uint8Array([\n 0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 30, 1, 28, 0, 65, 0,\n 253, 15, 253, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 253, 186, 1, 26, 11\n ]));\n } catch (e) {\n return false;\n }\n};\n\nconst getWasmFileName = (useSimd: boolean, useThreads: boolean) => {\n if (useSimd) {\n if (!BUILD_DEFS.DISABLE_TRAINING) {\n return 'ort-training-wasm-simd.wasm';\n }\n return useThreads ? 'ort-wasm-simd-threaded.wasm' : 'ort-wasm-simd.wasm';\n } else {\n return useThreads ? 'ort-wasm-threaded.wasm' : 'ort-wasm.wasm';\n }\n};\n\nexport const initializeWebAssembly = async(flags: Env.WebAssemblyFlags): Promise => {\n if (initialized) {\n return Promise.resolve();\n }\n if (initializing) {\n throw new Error('multiple calls to \\'initializeWebAssembly()\\' detected.');\n }\n if (aborted) {\n throw new Error('previous call to \\'initializeWebAssembly()\\' failed.');\n }\n\n initializing = true;\n\n // wasm flags are already initialized\n const timeout = flags.initTimeout!;\n const numThreads = flags.numThreads!;\n const simd = flags.simd!;\n\n const useThreads = isMultiThreadSupported(numThreads);\n const useSimd = simd && isSimdSupported();\n\n const wasmPaths = flags.wasmPaths;\n const wasmPrefixOverride = typeof wasmPaths === 'string' ? wasmPaths : undefined;\n const wasmFileName = getWasmFileName(useSimd, useThreads);\n const wasmPathOverride = typeof wasmPaths === 'object' ? wasmPaths[wasmFileName] : undefined;\n\n let isTimeout = false;\n\n const tasks: Array> = [];\n\n // promise for timeout\n if (timeout > 0) {\n tasks.push(new Promise((resolve) => {\n setTimeout(() => {\n isTimeout = true;\n resolve();\n }, timeout);\n }));\n }\n\n // promise for module initialization\n tasks.push(new Promise((resolve, reject) => {\n const factory = useThreads ? ortWasmFactoryThreaded : ortWasmFactory;\n const config: Partial = {\n locateFile: (fileName: string, scriptDirectory: string) => {\n if (!BUILD_DEFS.DISABLE_WASM_THREAD && useThreads && fileName.endsWith('.worker.js') &&\n typeof Blob !== 'undefined') {\n return URL.createObjectURL(new Blob(\n [\n // This require() function is handled by esbuild plugin to load file content as string.\n // eslint-disable-next-line @typescript-eslint/no-require-imports\n require('./binding/ort-wasm-threaded.worker.js')\n ],\n {type: 'text/javascript'}));\n }\n\n if (fileName.endsWith('.wasm')) {\n if (wasmPathOverride) {\n return wasmPathOverride;\n }\n\n const prefix = wasmPrefixOverride ?? scriptDirectory;\n\n if (!BUILD_DEFS.DISABLE_WEBGPU) {\n if (wasmFileName === 'ort-wasm-simd.wasm') {\n return prefix + 'ort-wasm-simd.jsep.wasm';\n } else if (wasmFileName === 'ort-wasm-simd-threaded.wasm') {\n return prefix + 'ort-wasm-simd-threaded.jsep.wasm';\n }\n }\n\n return prefix + wasmFileName;\n }\n\n return scriptDirectory + fileName;\n }\n };\n\n if (!BUILD_DEFS.DISABLE_WASM_THREAD && useThreads) {\n config.numThreads = numThreads;\n if (typeof Blob === 'undefined') {\n config.mainScriptUrlOrBlob = path.join(__dirname, 'ort-wasm-threaded.js');\n } else {\n const scriptSourceCode = `var ortWasmThreaded=${factory.toString()};`;\n config.mainScriptUrlOrBlob = new Blob([scriptSourceCode], {type: 'text/javascript'});\n }\n }\n\n factory(config).then(\n // wasm module initialized successfully\n module => {\n initializing = false;\n initialized = true;\n wasm = module;\n resolve();\n },\n // wasm module failed to initialize\n (what) => {\n initializing = false;\n aborted = true;\n reject(what);\n });\n }));\n\n await Promise.race(tasks);\n\n if (isTimeout) {\n throw new Error(`WebAssembly backend initializing failed due to timeout: ${timeout}ms`);\n }\n};\n\nexport const getInstance = (): OrtWasmModule => {\n if (initialized && wasm) {\n return wasm;\n }\n\n throw new Error('WebAssembly is not initialized yet.');\n};\n\nexport const dispose = (): void => {\n if (initialized && !initializing && !aborted) {\n initializing = true;\n\n (wasm as OrtWasmThreadedModule).PThread?.terminateAllThreads();\n wasm = undefined;\n\n initializing = false;\n initialized = false;\n aborted = true;\n }\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {getInstance} from './wasm-factory';\n\nexport const allocWasmString = (data: string, allocs: number[]): number => {\n const wasm = getInstance();\n\n const dataLength = wasm.lengthBytesUTF8(data) + 1;\n const dataOffset = wasm._malloc(dataLength);\n wasm.stringToUTF8(data, dataOffset, dataLength);\n allocs.push(dataOffset);\n\n return dataOffset;\n};\n\ninterface ExtraOptionsHandler {\n (name: string, value: string): void;\n}\n\nexport const iterateExtraOptions =\n (options: Record, prefix: string, seen: WeakSet>,\n handler: ExtraOptionsHandler): void => {\n if (typeof options == 'object' && options !== null) {\n if (seen.has(options)) {\n throw new Error('Circular reference in options');\n } else {\n seen.add(options);\n }\n }\n\n Object.entries(options).forEach(([key, value]) => {\n const name = (prefix) ? prefix + key : key;\n if (typeof value === 'object') {\n iterateExtraOptions(value as Record, name + '.', seen, handler);\n } else if (typeof value === 'string' || typeof value === 'number') {\n handler(name, value.toString());\n } else if (typeof value === 'boolean') {\n handler(name, (value) ? '1' : '0');\n } else {\n throw new Error(`Can't handle extra config type: ${typeof value}`);\n }\n });\n };\n\n/**\n * check web assembly API's last error and throw error if any error occurred.\n * @param message a message used when an error occurred.\n */\nexport const checkLastError = (message: string): void => {\n const wasm = getInstance();\n\n const stack = wasm.stackSave();\n try {\n const paramsOffset = wasm.stackAlloc(8);\n wasm._OrtGetLastError(paramsOffset, paramsOffset + 4);\n const errorCode = wasm.HEAP32[paramsOffset / 4];\n const errorMessagePointer = wasm.HEAPU32[paramsOffset / 4 + 1];\n const errorMessage = errorMessagePointer ? wasm.UTF8ToString(errorMessagePointer) : '';\n throw new Error(`${message} ERROR_CODE: ${errorCode}, ERROR_MESSAGE: ${errorMessage}`);\n } finally {\n wasm.stackRestore(stack);\n }\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession} from 'onnxruntime-common';\n\nimport {getInstance} from './wasm-factory';\nimport {allocWasmString, checkLastError, iterateExtraOptions} from './wasm-utils';\n\nexport const setRunOptions = (options: InferenceSession.RunOptions): [number, number[]] => {\n const wasm = getInstance();\n let runOptionsHandle = 0;\n const allocs: number[] = [];\n\n const runOptions: InferenceSession.RunOptions = options || {};\n\n try {\n if (options?.logSeverityLevel === undefined) {\n runOptions.logSeverityLevel = 2; // Default to warning\n } else if (\n typeof options.logSeverityLevel !== 'number' || !Number.isInteger(options.logSeverityLevel) ||\n options.logSeverityLevel < 0 || options.logSeverityLevel > 4) {\n throw new Error(`log serverity level is not valid: ${options.logSeverityLevel}`);\n }\n\n if (options?.logVerbosityLevel === undefined) {\n runOptions.logVerbosityLevel = 0; // Default to 0\n } else if (typeof options.logVerbosityLevel !== 'number' || !Number.isInteger(options.logVerbosityLevel)) {\n throw new Error(`log verbosity level is not valid: ${options.logVerbosityLevel}`);\n }\n\n if (options?.terminate === undefined) {\n runOptions.terminate = false;\n }\n\n let tagDataOffset = 0;\n if (options?.tag !== undefined) {\n tagDataOffset = allocWasmString(options.tag, allocs);\n }\n\n runOptionsHandle = wasm._OrtCreateRunOptions(\n runOptions.logSeverityLevel!, runOptions.logVerbosityLevel!, !!runOptions.terminate!, tagDataOffset);\n if (runOptionsHandle === 0) {\n checkLastError('Can\\'t create run options.');\n }\n\n if (options?.extra !== undefined) {\n iterateExtraOptions(options.extra, '', new WeakSet>(), (key, value) => {\n const keyDataOffset = allocWasmString(key, allocs);\n const valueDataOffset = allocWasmString(value, allocs);\n\n if (wasm._OrtAddRunConfigEntry(runOptionsHandle, keyDataOffset, valueDataOffset) !== 0) {\n checkLastError(`Can't set a run config entry: ${key} - ${value}.`);\n }\n });\n }\n\n return [runOptionsHandle, allocs];\n } catch (e) {\n if (runOptionsHandle !== 0) {\n wasm._OrtReleaseRunOptions(runOptionsHandle);\n }\n allocs.forEach(alloc => wasm._free(alloc));\n throw e;\n }\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession} from 'onnxruntime-common';\n\nimport {getInstance} from './wasm-factory';\nimport {allocWasmString, checkLastError, iterateExtraOptions} from './wasm-utils';\n\nconst getGraphOptimzationLevel = (graphOptimizationLevel: string|unknown): number => {\n switch (graphOptimizationLevel) {\n case 'disabled':\n return 0;\n case 'basic':\n return 1;\n case 'extended':\n return 2;\n case 'all':\n return 99;\n default:\n throw new Error(`unsupported graph optimization level: ${graphOptimizationLevel}`);\n }\n};\n\nconst getExecutionMode = (executionMode: 'sequential'|'parallel'): number => {\n switch (executionMode) {\n case 'sequential':\n return 0;\n case 'parallel':\n return 1;\n default:\n throw new Error(`unsupported execution mode: ${executionMode}`);\n }\n};\n\nconst appendDefaultOptions = (options: InferenceSession.SessionOptions): void => {\n if (!options.extra) {\n options.extra = {};\n }\n if (!options.extra.session) {\n options.extra.session = {};\n }\n const session = options.extra.session as Record;\n if (!session.use_ort_model_bytes_directly) {\n // eslint-disable-next-line camelcase\n session.use_ort_model_bytes_directly = '1';\n }\n\n // if using JSEP with WebGPU, always disable memory pattern\n if (options.executionProviders &&\n options.executionProviders.some(ep => (typeof ep === 'string' ? ep : ep.name) === 'webgpu')) {\n options.enableMemPattern = false;\n }\n};\n\nconst setExecutionProviders =\n (sessionOptionsHandle: number, executionProviders: readonly InferenceSession.ExecutionProviderConfig[],\n allocs: number[]): void => {\n for (const ep of executionProviders) {\n let epName = typeof ep === 'string' ? ep : ep.name;\n\n // check EP name\n switch (epName) {\n case 'webnn':\n epName = 'WEBNN';\n if (typeof ep !== 'string') {\n const webnnOptions = ep as InferenceSession.WebNNExecutionProviderOption;\n if (webnnOptions?.deviceType) {\n const keyDataOffset = allocWasmString('deviceType', allocs);\n const valueDataOffset = allocWasmString(webnnOptions.deviceType, allocs);\n if (getInstance()._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !==\n 0) {\n checkLastError(`Can't set a session config entry: 'deviceType' - ${webnnOptions.deviceType}.`);\n }\n }\n if (webnnOptions?.numThreads) {\n let numThreads = webnnOptions.numThreads;\n // Just ignore invalid webnnOptions.numThreads.\n if (typeof numThreads != 'number' || !Number.isInteger(numThreads) || numThreads < 0) {\n numThreads = 0;\n }\n const keyDataOffset = allocWasmString('numThreads', allocs);\n const valueDataOffset = allocWasmString(numThreads.toString(), allocs);\n if (getInstance()._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !==\n 0) {\n checkLastError(`Can't set a session config entry: 'numThreads' - ${webnnOptions.numThreads}.`);\n }\n }\n if (webnnOptions?.powerPreference) {\n const keyDataOffset = allocWasmString('powerPreference', allocs);\n const valueDataOffset = allocWasmString(webnnOptions.powerPreference, allocs);\n if (getInstance()._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !==\n 0) {\n checkLastError(\n `Can't set a session config entry: 'powerPreference' - ${webnnOptions.powerPreference}.`);\n }\n }\n }\n break;\n case 'webgpu':\n epName = 'JS';\n if (typeof ep !== 'string') {\n const webgpuOptions = ep as InferenceSession.WebGpuExecutionProviderOption;\n if (webgpuOptions?.preferredLayout) {\n if (webgpuOptions.preferredLayout !== 'NCHW' && webgpuOptions.preferredLayout !== 'NHWC') {\n throw new Error(`preferredLayout must be either 'NCHW' or 'NHWC': ${webgpuOptions.preferredLayout}`);\n }\n const keyDataOffset = allocWasmString('preferredLayout', allocs);\n const valueDataOffset = allocWasmString(webgpuOptions.preferredLayout, allocs);\n if (getInstance()._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !==\n 0) {\n checkLastError(\n `Can't set a session config entry: 'preferredLayout' - ${webgpuOptions.preferredLayout}.`);\n }\n }\n }\n break;\n case 'wasm':\n case 'cpu':\n continue;\n default:\n throw new Error(`not supported execution provider: ${epName}`);\n }\n\n const epNameDataOffset = allocWasmString(epName, allocs);\n if (getInstance()._OrtAppendExecutionProvider(sessionOptionsHandle, epNameDataOffset) !== 0) {\n checkLastError(`Can't append execution provider: ${epName}.`);\n }\n }\n };\n\nexport const setSessionOptions = (options?: InferenceSession.SessionOptions): [number, number[]] => {\n const wasm = getInstance();\n let sessionOptionsHandle = 0;\n const allocs: number[] = [];\n\n const sessionOptions: InferenceSession.SessionOptions = options || {};\n appendDefaultOptions(sessionOptions);\n\n try {\n const graphOptimizationLevel = getGraphOptimzationLevel(sessionOptions.graphOptimizationLevel ?? 'all');\n const executionMode = getExecutionMode(sessionOptions.executionMode ?? 'sequential');\n const logIdDataOffset =\n typeof sessionOptions.logId === 'string' ? allocWasmString(sessionOptions.logId, allocs) : 0;\n\n const logSeverityLevel = sessionOptions.logSeverityLevel ?? 2; // Default to 2 - warning\n if (!Number.isInteger(logSeverityLevel) || logSeverityLevel < 0 || logSeverityLevel > 4) {\n throw new Error(`log serverity level is not valid: ${logSeverityLevel}`);\n }\n\n const logVerbosityLevel = sessionOptions.logVerbosityLevel ?? 0; // Default to 0 - verbose\n if (!Number.isInteger(logVerbosityLevel) || logVerbosityLevel < 0 || logVerbosityLevel > 4) {\n throw new Error(`log verbosity level is not valid: ${logVerbosityLevel}`);\n }\n\n const optimizedModelFilePathOffset = typeof sessionOptions.optimizedModelFilePath === 'string' ?\n allocWasmString(sessionOptions.optimizedModelFilePath, allocs) :\n 0;\n\n sessionOptionsHandle = wasm._OrtCreateSessionOptions(\n graphOptimizationLevel, !!sessionOptions.enableCpuMemArena, !!sessionOptions.enableMemPattern, executionMode,\n !!sessionOptions.enableProfiling, 0, logIdDataOffset, logSeverityLevel, logVerbosityLevel,\n optimizedModelFilePathOffset);\n if (sessionOptionsHandle === 0) {\n checkLastError('Can\\'t create session options.');\n }\n\n if (sessionOptions.executionProviders) {\n setExecutionProviders(sessionOptionsHandle, sessionOptions.executionProviders, allocs);\n }\n\n if (sessionOptions.enableGraphCapture !== undefined) {\n if (typeof sessionOptions.enableGraphCapture !== 'boolean') {\n throw new Error(`enableGraphCapture must be a boolean value: ${sessionOptions.enableGraphCapture}`);\n }\n const keyDataOffset = allocWasmString('enableGraphCapture', allocs);\n const valueDataOffset = allocWasmString(sessionOptions.enableGraphCapture.toString(), allocs);\n if (wasm._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !== 0) {\n checkLastError(\n `Can't set a session config entry: 'enableGraphCapture' - ${sessionOptions.enableGraphCapture}.`);\n }\n }\n\n if (sessionOptions.freeDimensionOverrides) {\n for (const [name, value] of Object.entries(sessionOptions.freeDimensionOverrides)) {\n if (typeof name !== 'string') {\n throw new Error(`free dimension override name must be a string: ${name}`);\n }\n if (typeof value !== 'number' || !Number.isInteger(value) || value < 0) {\n throw new Error(`free dimension override value must be a non-negative integer: ${value}`);\n }\n const nameOffset = allocWasmString(name, allocs);\n if (wasm._OrtAddFreeDimensionOverride(sessionOptionsHandle, nameOffset, value) !== 0) {\n checkLastError(`Can't set a free dimension override: ${name} - ${value}.`);\n }\n }\n }\n\n if (sessionOptions.extra !== undefined) {\n iterateExtraOptions(sessionOptions.extra, '', new WeakSet>(), (key, value) => {\n const keyDataOffset = allocWasmString(key, allocs);\n const valueDataOffset = allocWasmString(value, allocs);\n\n if (wasm._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !== 0) {\n checkLastError(`Can't set a session config entry: ${key} - ${value}.`);\n }\n });\n }\n\n return [sessionOptionsHandle, allocs];\n } catch (e) {\n if (sessionOptionsHandle !== 0) {\n wasm._OrtReleaseSessionOptions(sessionOptionsHandle);\n }\n allocs.forEach(alloc => wasm._free(alloc));\n throw e;\n }\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from 'onnxruntime-common';\n\n// a dummy type declaration for Float16Array in case any polyfill is available.\ndeclare global {\n // eslint-disable-next-line @typescript-eslint/naming-convention, @typescript-eslint/no-explicit-any\n const Float16Array: any;\n}\n\n// This file includes common definitions. They do NOT have dependency on the WebAssembly instance.\n\n/**\n * Copied from ONNX definition. Use this to drop dependency 'onnx_proto' to decrease compiled .js file size.\n */\nexport const enum DataType {\n undefined = 0,\n float = 1,\n uint8 = 2,\n int8 = 3,\n uint16 = 4,\n int16 = 5,\n int32 = 6,\n int64 = 7,\n string = 8,\n bool = 9,\n float16 = 10,\n double = 11,\n uint32 = 12,\n uint64 = 13,\n complex64 = 14,\n complex128 = 15,\n bfloat16 = 16\n}\n\n/**\n * Map string tensor data to enum value\n */\nexport const tensorDataTypeStringToEnum = (type: string): DataType => {\n switch (type) {\n case 'int8':\n return DataType.int8;\n case 'uint8':\n return DataType.uint8;\n case 'bool':\n return DataType.bool;\n case 'int16':\n return DataType.int16;\n case 'uint16':\n return DataType.uint16;\n case 'int32':\n return DataType.int32;\n case 'uint32':\n return DataType.uint32;\n case 'float16':\n return DataType.float16;\n case 'float32':\n return DataType.float;\n case 'float64':\n return DataType.double;\n case 'string':\n return DataType.string;\n case 'int64':\n return DataType.int64;\n case 'uint64':\n return DataType.uint64;\n\n default:\n throw new Error(`unsupported data type: ${type}`);\n }\n};\n\n/**\n * Map enum value to string tensor data\n */\nexport const tensorDataTypeEnumToString = (typeProto: DataType): Tensor.Type => {\n switch (typeProto) {\n case DataType.int8:\n return 'int8';\n case DataType.uint8:\n return 'uint8';\n case DataType.bool:\n return 'bool';\n case DataType.int16:\n return 'int16';\n case DataType.uint16:\n return 'uint16';\n case DataType.int32:\n return 'int32';\n case DataType.uint32:\n return 'uint32';\n case DataType.float16:\n return 'float16';\n case DataType.float:\n return 'float32';\n case DataType.double:\n return 'float64';\n case DataType.string:\n return 'string';\n case DataType.int64:\n return 'int64';\n case DataType.uint64:\n return 'uint64';\n\n default:\n throw new Error(`unsupported data type: ${typeProto}`);\n }\n};\n\n/**\n * get tensor element size in bytes by the given data type\n * @returns size in integer or undefined if the data type is not supported\n */\nexport const getTensorElementSize = (dateType: number): number|\n undefined => [undefined, 4, 1, 1, 2, 2, 4, 8, undefined, 1, 2, 8, 4, 8, undefined, undefined, undefined][dateType];\n\n/**\n * get typed array constructor by the given tensor type\n */\nexport const tensorTypeToTypedArrayConstructor = (type: Tensor.Type): Float32ArrayConstructor|Uint8ArrayConstructor|\n Int8ArrayConstructor|Uint16ArrayConstructor|Int16ArrayConstructor|Int32ArrayConstructor|BigInt64ArrayConstructor|\n Uint8ArrayConstructor|Float64ArrayConstructor|Uint32ArrayConstructor|BigUint64ArrayConstructor => {\n switch (type) {\n case 'float16':\n // allow Float16Array polyfill.\n return typeof Float16Array !== 'undefined' && Float16Array.from ? Float16Array : Uint16Array;\n case 'float32':\n return Float32Array;\n case 'uint8':\n return Uint8Array;\n case 'int8':\n return Int8Array;\n case 'uint16':\n return Uint16Array;\n case 'int16':\n return Int16Array;\n case 'int32':\n return Int32Array;\n case 'bool':\n return Uint8Array;\n case 'float64':\n return Float64Array;\n case 'uint32':\n return Uint32Array;\n case 'int64':\n return BigInt64Array;\n case 'uint64':\n return BigUint64Array;\n default:\n throw new Error(`unsupported type: ${type}`);\n }\n };\n\n/**\n * Map string log level to integer value\n */\nexport const logLevelStringToEnum = (logLevel?: 'verbose'|'info'|'warning'|'error'|'fatal'): number => {\n switch (logLevel) {\n case 'verbose':\n return 0;\n case 'info':\n return 1;\n case 'warning':\n return 2;\n case 'error':\n return 3;\n case 'fatal':\n return 4;\n default:\n throw new Error(`unsupported logging level: ${logLevel}`);\n }\n};\n\n/**\n * Check whether the given tensor type is supported by GPU buffer\n */\nexport const isGpuBufferSupportedType = (type: Tensor.Type): type is Tensor.GpuBufferDataTypes => type === 'float32' ||\n type === 'float16' || type === 'int32' || type === 'int64' || type === 'uint32' || type === 'uint8' ||\n type === 'bool';\n\n/**\n * Map string data location to integer value\n */\nexport const dataLocationStringToEnum = (location: Tensor.DataLocation): number => {\n switch (location) {\n case 'none':\n return 0;\n case 'cpu':\n return 1;\n case 'cpu-pinned':\n return 2;\n case 'texture':\n return 3;\n case 'gpu-buffer':\n return 4;\n default:\n throw new Error(`unsupported data location: ${location}`);\n }\n};\n\n/**\n * Map integer data location to string value\n */\nexport const dataLocationEnumToString = (location: number): Tensor.DataLocation|undefined =>\n (['none', 'cpu', 'cpu-pinned', 'texture', 'gpu-buffer'] as const)[location];\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport * as fs from 'fs';\nimport {readFile} from 'node:fs/promises';\n\n/**\n * Load a file into a Uint8Array.\n *\n * @param file - the file to load. Can be a URL/path, a Blob, an ArrayBuffer, or a Uint8Array.\n * @returns a Uint8Array containing the file data.\n */\nexport const loadFile = async(file: string|Blob|ArrayBufferLike|Uint8Array): Promise => {\n if (typeof file === 'string') {\n if (typeof process !== 'undefined' && process.versions && process.versions.node) {\n // load file into ArrayBuffer in Node.js\n try {\n return new Uint8Array(await readFile(file));\n } catch (e) {\n if (e.code === 'ERR_FS_FILE_TOO_LARGE') {\n // file is too large, use fs.createReadStream instead\n const stream = fs.createReadStream(file);\n const chunks: Uint8Array[] = [];\n for await (const chunk of stream) {\n chunks.push(chunk);\n }\n return new Uint8Array(Buffer.concat(chunks));\n }\n throw e;\n }\n } else {\n // load file into ArrayBuffer in browsers\n const response = await fetch(file);\n if (!response.ok) {\n throw new Error(`failed to load external data file: ${file}`);\n }\n const contentLengthHeader = response.headers.get('Content-Length');\n const fileSize = contentLengthHeader ? parseInt(contentLengthHeader, 10) : 0;\n if (fileSize < 1073741824 /* 1GB */) {\n // when Content-Length header is not set, we cannot determine the file size. We assume it is small enough to\n // load into memory.\n return new Uint8Array(await response.arrayBuffer());\n } else {\n // file is too large, use stream instead\n if (!response.body) {\n throw new Error(`failed to load external data file: ${file}, no response body.`);\n }\n const reader = response.body.getReader();\n\n let buffer;\n try {\n // try to create ArrayBuffer directly\n buffer = new ArrayBuffer(fileSize);\n } catch (e) {\n if (e instanceof RangeError) {\n // use WebAssembly Memory to allocate larger ArrayBuffer\n const pages = Math.ceil(fileSize / 65536);\n buffer = new WebAssembly.Memory({initial: pages, maximum: pages}).buffer;\n } else {\n throw e;\n }\n }\n\n let offset = 0;\n // eslint-disable-next-line no-constant-condition\n while (true) {\n const {done, value} = await reader.read();\n if (done) {\n break;\n }\n const chunkSize = value.byteLength;\n const chunk = new Uint8Array(buffer, offset, chunkSize);\n chunk.set(value);\n offset += chunkSize;\n }\n return new Uint8Array(buffer, 0, fileSize);\n }\n }\n\n } else if (file instanceof Blob) {\n return new Uint8Array(await file.arrayBuffer());\n } else if (file instanceof Uint8Array) {\n return file;\n } else {\n return new Uint8Array(file);\n }\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Env, InferenceSession, Tensor} from 'onnxruntime-common';\n\nimport {SerializableInternalBuffer, SerializableSessionMetadata, SerializableTensorMetadata, TensorMetadata} from './proxy-messages';\nimport {setRunOptions} from './run-options';\nimport {setSessionOptions} from './session-options';\nimport {dataLocationStringToEnum, getTensorElementSize, isGpuBufferSupportedType, logLevelStringToEnum, tensorDataTypeEnumToString, tensorDataTypeStringToEnum, tensorTypeToTypedArrayConstructor} from './wasm-common';\nimport {getInstance} from './wasm-factory';\nimport {allocWasmString, checkLastError} from './wasm-utils';\nimport {loadFile} from './wasm-utils-load-file';\n\n// #region Initializations\n\n/**\n * There are 4 different \"initialization\" steps for ORT. They happen in different places and different time.\n *\n * 1. JavaScript initialization for onnxruntime-common and onnxruntime-web.\n * This is the first initialization step. In this step, onnxruntime-web calls onnxruntime-common's registerBackend()\n * function multiple times to register all the available backends. The backend registration is very fast. It only\n * registers the backend name with the uninitialized backend object. No heavy initialization is done in this step.\n * Refer to web/lib/index.ts for the backend registration.\n *\n * 2. WebAssembly artifact initialization.\n * This happens when any registered wasm backend is used for the first time (ie. `ort.InferenceSession.create()` or\n * `ort.TrainingSession.create()` is called). In this step, onnxruntime-web does the followings:\n * - create a proxy worker and make sure the proxy worker is ready to receive messages, if proxy is enabled.\n * - perform feature detection, locate correct WebAssembly artifact path and call the Emscripten generated\n * JavaScript code to initialize the WebAssembly runtime.\n * - if proxy is enabled, this step happens in the proxy worker using message 'init-wasm'.\n * - downloading the 'ort-wasm{...}.wasm' file is done in this step.\n * - if multi-thread is enabled, one or more webworker will be created to initialize the PThread threadpool.\n *\n * 3. ORT environment initialization.\n * This happens after step 2. In this step, onnxruntime-web performs ONNX Runtime environment initialization.\n * Function `_OrtInit()` is called in this step.\n * - if proxy is enabled, this step happens in the proxy worker using message 'init-ort'.\n * - logging level (ort.env.logLevel) and thread number (ort.env.wasm.numThreads) are set in this step.\n *\n * 4. Session initialization.\n * This happens when `ort.InferenceSession.create()` or `ort.TrainingSession.create()` is called. Unlike the first 3\n * steps (they only called once), this step will be done for each session. In this step, onnxruntime-web does the\n * followings:\n * If the parameter is a URL:\n * - download the model data from the URL.\n * - copy the model data to the WASM heap. (proxy: 'copy-from')\n * - dereference the model buffer. This step allows the original ArrayBuffer to be garbage collected.\n * - call `_OrtCreateSession()` to create the session. (proxy: 'create')\n *\n * If the parameter is a Uint8Array object:\n * - copy the model data to the WASM heap. (proxy: 'copy-from')\n * - call `_OrtCreateSession()` to create the session. (proxy: 'create')\n *\n *\n */\n\n/**\n * initialize ORT environment.\n *\n * @param numThreads SetGlobalIntraOpNumThreads(numThreads)\n * @param loggingLevel CreateEnv(static_cast(logging_level))\n */\nconst initOrt = (numThreads: number, loggingLevel: number): void => {\n const errorCode = getInstance()._OrtInit(numThreads, loggingLevel);\n if (errorCode !== 0) {\n checkLastError('Can\\'t initialize onnxruntime.');\n }\n};\n\n/**\n * intialize runtime environment.\n * @param env passed in the environment config object.\n */\nexport const initRuntime = async(env: Env): Promise => {\n // init ORT\n initOrt(env.wasm.numThreads!, logLevelStringToEnum(env.logLevel));\n};\n\n/**\n * perform EP specific initialization.\n *\n * @param env\n * @param epName\n */\nexport const initEp = async(env: Env, epName: string): Promise => {\n if (!BUILD_DEFS.DISABLE_WEBGPU) {\n // eslint-disable-next-line @typescript-eslint/no-require-imports, @typescript-eslint/no-var-requires\n const initJsep = require('./jsep/init').init;\n\n if (epName === 'webgpu') {\n // perform WebGPU availability check\n if (typeof navigator === 'undefined' || !navigator.gpu) {\n throw new Error('WebGPU is not supported in current environment');\n }\n\n let adapter = env.webgpu.adapter as GPUAdapter | null;\n if (!adapter) {\n // if adapter is not set, request a new adapter.\n const powerPreference = env.webgpu.powerPreference;\n if (powerPreference !== undefined && powerPreference !== 'low-power' &&\n powerPreference !== 'high-performance') {\n throw new Error(`Invalid powerPreference setting: \"${powerPreference}\"`);\n }\n const forceFallbackAdapter = env.webgpu.forceFallbackAdapter;\n if (forceFallbackAdapter !== undefined && typeof forceFallbackAdapter !== 'boolean') {\n throw new Error(`Invalid forceFallbackAdapter setting: \"${forceFallbackAdapter}\"`);\n }\n adapter = await navigator.gpu.requestAdapter({powerPreference, forceFallbackAdapter});\n if (!adapter) {\n throw new Error(\n 'Failed to get GPU adapter. ' +\n 'You may need to enable flag \"--enable-unsafe-webgpu\" if you are using Chrome.');\n }\n } else {\n // if adapter is set, validate it.\n if (typeof adapter.limits !== 'object' || typeof adapter.features !== 'object' ||\n typeof adapter.requestDevice !== 'function') {\n throw new Error('Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.');\n }\n }\n\n if (!env.wasm.simd) {\n throw new Error(\n 'Not supported for WebGPU=ON and SIMD=OFF. Please set `env.wasm.simd` to true when using `webgpu` EP');\n }\n\n await initJsep('webgpu', getInstance(), env, adapter);\n }\n if (epName === 'webnn') {\n // perform WebNN availability check\n if (typeof navigator === 'undefined' || !(navigator as unknown as {ml: unknown}).ml) {\n throw new Error('WebNN is not supported in current environment');\n }\n\n await initJsep('webnn', getInstance(), env);\n }\n }\n};\n\n// #endregion Initializations\n\n/**\n * valid data locations for input/output tensors.\n */\ntype SupportedTensorDataLocationForInputOutput = 'cpu'|'cpu-pinned'|'gpu-buffer';\n\ntype IOBindingState = {\n /**\n * the handle of IO binding.\n */\n readonly handle: number;\n\n /**\n * the preferred location for each output tensor.\n *\n * value is one of 'cpu', 'cpu-pinned', 'gpu-buffer'.\n */\n readonly outputPreferredLocations: readonly SupportedTensorDataLocationForInputOutput[];\n\n /**\n * enum value of the preferred location for each output tensor.\n */\n readonly outputPreferredLocationsEncoded: readonly number[];\n};\n\n/**\n * tuple elements are: InferenceSession ID; inputNamesUTF8Encoded; outputNamesUTF8Encoded; bindingState\n */\ntype SessionMetadata = [\n inferenceSessionId: number, inputNamesUTF8Encoded: number[], outputNamesUTF8Encoded: number[],\n bindingState: IOBindingState|null, enableGraphCapture: boolean, inputOutputBound: boolean\n];\n\nconst activeSessions = new Map();\n\n/**\n * get the input/output count of the session.\n * @param sessionHandle the handle representing the session. should be non-zero.\n * @returns a tuple including 2 numbers, representing the input count and output count.\n */\nconst getSessionInputOutputCount = (sessionHandle: number): [number, number] => {\n const wasm = getInstance();\n const stack = wasm.stackSave();\n try {\n const dataOffset = wasm.stackAlloc(8);\n const errorCode = wasm._OrtGetInputOutputCount(sessionHandle, dataOffset, dataOffset + 4);\n if (errorCode !== 0) {\n checkLastError('Can\\'t get session input/output count.');\n }\n return [wasm.HEAP32[dataOffset / 4], wasm.HEAP32[dataOffset / 4 + 1]];\n } finally {\n wasm.stackRestore(stack);\n }\n};\n\n/**\n * allocate the memory and memcpy the external buffer.\n *\n * @param model - the external buffer containing the model data. Must not be the same buffer as the WASM heap.\n * @returns a 2-elements tuple - the pointer and size of the allocated buffer\n */\nexport const copyFromExternalBuffer = (model: Uint8Array): [number, number] => {\n const wasm = getInstance();\n const modelDataOffset = wasm._malloc(model.byteLength);\n if (modelDataOffset === 0) {\n throw new Error(`Can't create a session. failed to allocate a buffer of size ${model.byteLength}.`);\n }\n wasm.HEAPU8.set(model, modelDataOffset);\n return [modelDataOffset, model.byteLength];\n};\n\n/**\n * create an inference session from a model data buffer.\n *\n * @param modelData - either a Uint8Array object representing the model data, or a 2-elements tuple containing the\n * pointer and size of the model data buffer.\n * @param options an optional session options object.\n * @returns a 3-elements tuple containing [session handle, input names, output names]\n */\nexport const createSession = async(\n modelData: Uint8Array|SerializableInternalBuffer,\n options?: InferenceSession.SessionOptions): Promise => {\n let modelDataOffset: number, modelDataLength: number;\n const wasm = getInstance();\n\n if (Array.isArray(modelData)) {\n // if model data is an array, it must be a 2-elements tuple containing the pointer and size of the model data\n [modelDataOffset, modelDataLength] = modelData;\n } else if (modelData.buffer === wasm.HEAPU8.buffer) {\n // if model data uses the same buffer as the WASM heap, we don't need to copy it.\n [modelDataOffset, modelDataLength] = [modelData.byteOffset, modelData.byteLength];\n } else {\n // otherwise, copy the model data to the WASM heap.\n [modelDataOffset, modelDataLength] = copyFromExternalBuffer(modelData);\n }\n\n let sessionHandle = 0;\n let sessionOptionsHandle = 0;\n let ioBindingHandle = 0;\n let allocs: number[] = [];\n const inputNamesUTF8Encoded = [];\n const outputNamesUTF8Encoded = [];\n\n try {\n [sessionOptionsHandle, allocs] = setSessionOptions(options);\n\n if (options?.externalData && wasm.mountExternalData) {\n const loadingPromises = [];\n for (const file of options.externalData) {\n const path = typeof file === 'string' ? file : file.path;\n loadingPromises.push(loadFile(typeof file === 'string' ? file : file.data).then(data => {\n wasm.mountExternalData!(path, data);\n }));\n }\n\n // wait for all external data files to be loaded\n await Promise.all(loadingPromises);\n }\n\n sessionHandle = await wasm._OrtCreateSession(modelDataOffset, modelDataLength, sessionOptionsHandle);\n if (sessionHandle === 0) {\n checkLastError('Can\\'t create a session.');\n }\n\n const [inputCount, outputCount] = getSessionInputOutputCount(sessionHandle);\n\n const enableGraphCapture = !!options?.enableGraphCapture;\n\n const inputNames = [];\n const outputNames = [];\n const outputPreferredLocations: SupportedTensorDataLocationForInputOutput[] = [];\n for (let i = 0; i < inputCount; i++) {\n const name = wasm._OrtGetInputName(sessionHandle, i);\n if (name === 0) {\n checkLastError('Can\\'t get an input name.');\n }\n inputNamesUTF8Encoded.push(name);\n inputNames.push(wasm.UTF8ToString(name));\n }\n for (let i = 0; i < outputCount; i++) {\n const name = wasm._OrtGetOutputName(sessionHandle, i);\n if (name === 0) {\n checkLastError('Can\\'t get an output name.');\n }\n outputNamesUTF8Encoded.push(name);\n const nameString = wasm.UTF8ToString(name);\n outputNames.push(nameString);\n\n if (!BUILD_DEFS.DISABLE_WEBGPU) {\n if (enableGraphCapture && options?.preferredOutputLocation === undefined) {\n outputPreferredLocations.push('gpu-buffer');\n continue;\n }\n const location = typeof options?.preferredOutputLocation === 'string' ?\n options.preferredOutputLocation :\n options?.preferredOutputLocation?.[nameString] ?? 'cpu';\n if (location !== 'cpu' && location !== 'cpu-pinned' && location !== 'gpu-buffer') {\n throw new Error(`Not supported preferred output location: ${location}.`);\n }\n if (enableGraphCapture && location !== 'gpu-buffer') {\n throw new Error(`Not supported preferred output location: ${\n location}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);\n }\n outputPreferredLocations.push(location);\n }\n }\n\n // use IO binding only when at least one output is preffered to be on GPU.\n let bindingState: IOBindingState|null = null;\n if (!BUILD_DEFS.DISABLE_WEBGPU && outputPreferredLocations.some(l => l === 'gpu-buffer')) {\n ioBindingHandle = wasm._OrtCreateBinding(sessionHandle);\n if (ioBindingHandle === 0) {\n checkLastError('Can\\'t create IO binding.');\n }\n\n bindingState = {\n handle: ioBindingHandle,\n outputPreferredLocations,\n outputPreferredLocationsEncoded: outputPreferredLocations.map(l => dataLocationStringToEnum(l)),\n };\n }\n\n activeSessions.set(\n sessionHandle,\n [sessionHandle, inputNamesUTF8Encoded, outputNamesUTF8Encoded, bindingState, enableGraphCapture, false]);\n return [sessionHandle, inputNames, outputNames];\n } catch (e) {\n inputNamesUTF8Encoded.forEach(buf => wasm._OrtFree(buf));\n outputNamesUTF8Encoded.forEach(buf => wasm._OrtFree(buf));\n\n if (ioBindingHandle !== 0) {\n wasm._OrtReleaseBinding(ioBindingHandle);\n }\n\n if (sessionHandle !== 0) {\n wasm._OrtReleaseSession(sessionHandle);\n }\n throw e;\n } finally {\n wasm._free(modelDataOffset);\n if (sessionOptionsHandle !== 0) {\n wasm._OrtReleaseSessionOptions(sessionOptionsHandle);\n }\n allocs.forEach(alloc => wasm._free(alloc));\n\n // unmount external data if necessary\n wasm.unmountExternalData?.();\n }\n};\n\nexport const releaseSession = (sessionId: number): void => {\n const wasm = getInstance();\n const session = activeSessions.get(sessionId);\n if (!session) {\n throw new Error(`cannot release session. invalid session id: ${sessionId}`);\n }\n const [sessionHandle, inputNamesUTF8Encoded, outputNamesUTF8Encoded, ioBindingState, enableGraphCapture] = session;\n\n if (ioBindingState) {\n if (enableGraphCapture) {\n wasm._OrtClearBoundOutputs(ioBindingState.handle);\n }\n wasm._OrtReleaseBinding(ioBindingState.handle);\n }\n\n wasm.jsepOnReleaseSession?.(sessionId);\n\n inputNamesUTF8Encoded.forEach(buf => wasm._OrtFree(buf));\n outputNamesUTF8Encoded.forEach(buf => wasm._OrtFree(buf));\n wasm._OrtReleaseSession(sessionHandle);\n activeSessions.delete(sessionId);\n};\n\nexport const prepareInputOutputTensor =\n (tensor: TensorMetadata|null, tensorHandles: number[], allocs: number[], sessionId: number, index: number,\n enableGraphCapture = false): void => {\n if (!tensor) {\n tensorHandles.push(0);\n return;\n }\n\n const wasm = getInstance();\n\n const dataType = tensor[0];\n const dims = tensor[1];\n const location = tensor[3];\n\n let rawData: number;\n let dataByteLength: number;\n\n if (dataType === 'string' && location === 'gpu-buffer') {\n throw new Error('String tensor is not supported on GPU.');\n }\n\n if (enableGraphCapture && location !== 'gpu-buffer') {\n throw new Error(\n `External buffer must be provided for input/output index ${index} when enableGraphCapture is true.`);\n }\n\n if (location === 'gpu-buffer') {\n const gpuBuffer = tensor[2].gpuBuffer as GPUBuffer;\n const elementSizeInBytes = getTensorElementSize(tensorDataTypeStringToEnum(dataType))!;\n dataByteLength = dims.reduce((a, b) => a * b, 1) * elementSizeInBytes;\n\n const registerBuffer = wasm.jsepRegisterBuffer;\n if (!registerBuffer) {\n throw new Error('Tensor location \"gpu-buffer\" is not supported without using WebGPU.');\n }\n rawData = registerBuffer(sessionId, index, gpuBuffer, dataByteLength);\n } else {\n const data = tensor[2];\n\n if (Array.isArray(data)) {\n // string tensor\n dataByteLength = 4 * data.length;\n rawData = wasm._malloc(dataByteLength);\n allocs.push(rawData);\n let dataIndex = rawData / 4;\n for (let i = 0; i < data.length; i++) {\n if (typeof data[i] !== 'string') {\n throw new TypeError(`tensor data at index ${i} is not a string`);\n }\n wasm.HEAPU32[dataIndex++] = allocWasmString(data[i], allocs);\n }\n } else {\n dataByteLength = data.byteLength;\n rawData = wasm._malloc(dataByteLength);\n allocs.push(rawData);\n wasm.HEAPU8.set(new Uint8Array(data.buffer, data.byteOffset, dataByteLength), rawData);\n }\n }\n\n const stack = wasm.stackSave();\n const dimsOffset = wasm.stackAlloc(4 * dims.length);\n try {\n let dimIndex = dimsOffset / 4;\n dims.forEach(d => wasm.HEAP32[dimIndex++] = d);\n const tensor = wasm._OrtCreateTensor(\n tensorDataTypeStringToEnum(dataType), rawData, dataByteLength, dimsOffset, dims.length,\n dataLocationStringToEnum(location));\n if (tensor === 0) {\n checkLastError(`Can't create tensor for input/output. session=${sessionId}, index=${index}.`);\n }\n tensorHandles.push(tensor);\n } finally {\n wasm.stackRestore(stack);\n }\n };\n\n/**\n * perform inference run\n */\nexport const run = async(\n sessionId: number, inputIndices: number[], inputTensors: TensorMetadata[], outputIndices: number[],\n outputTensors: Array, options: InferenceSession.RunOptions): Promise => {\n const wasm = getInstance();\n const session = activeSessions.get(sessionId);\n if (!session) {\n throw new Error(`cannot run inference. invalid session id: ${sessionId}`);\n }\n const sessionHandle = session[0];\n const inputNamesUTF8Encoded = session[1];\n const outputNamesUTF8Encoded = session[2];\n const ioBindingState = session[3];\n const enableGraphCapture = session[4];\n const inputOutputBound = session[5];\n\n const inputCount = inputIndices.length;\n const outputCount = outputIndices.length;\n\n let runOptionsHandle = 0;\n let runOptionsAllocs: number[] = [];\n\n const inputTensorHandles: number[] = [];\n const outputTensorHandles: number[] = [];\n const inputOutputAllocs: number[] = [];\n\n const beforeRunStack = wasm.stackSave();\n const inputValuesOffset = wasm.stackAlloc(inputCount * 4);\n const inputNamesOffset = wasm.stackAlloc(inputCount * 4);\n const outputValuesOffset = wasm.stackAlloc(outputCount * 4);\n const outputNamesOffset = wasm.stackAlloc(outputCount * 4);\n\n try {\n [runOptionsHandle, runOptionsAllocs] = setRunOptions(options);\n\n // create input tensors\n for (let i = 0; i < inputCount; i++) {\n prepareInputOutputTensor(\n inputTensors[i], inputTensorHandles, inputOutputAllocs, sessionId, inputIndices[i], enableGraphCapture);\n }\n\n // create output tensors\n for (let i = 0; i < outputCount; i++) {\n prepareInputOutputTensor(\n outputTensors[i], outputTensorHandles, inputOutputAllocs, sessionId, inputCount + outputIndices[i],\n enableGraphCapture);\n }\n\n let inputValuesIndex = inputValuesOffset / 4;\n let inputNamesIndex = inputNamesOffset / 4;\n let outputValuesIndex = outputValuesOffset / 4;\n let outputNamesIndex = outputNamesOffset / 4;\n for (let i = 0; i < inputCount; i++) {\n wasm.HEAPU32[inputValuesIndex++] = inputTensorHandles[i];\n wasm.HEAPU32[inputNamesIndex++] = inputNamesUTF8Encoded[inputIndices[i]];\n }\n for (let i = 0; i < outputCount; i++) {\n wasm.HEAPU32[outputValuesIndex++] = outputTensorHandles[i];\n wasm.HEAPU32[outputNamesIndex++] = outputNamesUTF8Encoded[outputIndices[i]];\n }\n\n if (!BUILD_DEFS.DISABLE_WEBGPU && ioBindingState && !inputOutputBound) {\n const {handle, outputPreferredLocations, outputPreferredLocationsEncoded} = ioBindingState;\n\n if (inputNamesUTF8Encoded.length !== inputCount) {\n throw new Error(`input count from feeds (${\n inputCount}) is expected to be always equal to model's input count (${inputNamesUTF8Encoded.length}).`);\n }\n\n // process inputs\n for (let i = 0; i < inputCount; i++) {\n const index = inputIndices[i];\n const errorCode = await wasm._OrtBindInput(handle, inputNamesUTF8Encoded[index], inputTensorHandles[i]);\n if (errorCode !== 0) {\n checkLastError(`Can't bind input[${i}] for session=${sessionId}.`);\n }\n }\n\n // process pre-allocated outputs\n for (let i = 0; i < outputCount; i++) {\n const index = outputIndices[i];\n const location = outputTensors[i]?.[3]; // undefined means output is not pre-allocated.\n\n if (location) {\n // output is pre-allocated. bind the tensor.\n const errorCode = wasm._OrtBindOutput(handle, outputNamesUTF8Encoded[index], outputTensorHandles[i], 0);\n if (errorCode !== 0) {\n checkLastError(`Can't bind pre-allocated output[${i}] for session=${sessionId}.`);\n }\n } else {\n // output is not pre-allocated. reset preferred location.\n const errorCode =\n wasm._OrtBindOutput(handle, outputNamesUTF8Encoded[index], 0, outputPreferredLocationsEncoded[index]);\n if (errorCode !== 0) {\n checkLastError(`Can't bind output[${i}] to ${outputPreferredLocations[i]} for session=${sessionId}.`);\n }\n }\n }\n activeSessions.set(\n sessionId,\n [sessionHandle, inputNamesUTF8Encoded, outputNamesUTF8Encoded, ioBindingState, enableGraphCapture, true]);\n }\n\n wasm.jsepOnRunStart?.(sessionHandle);\n let errorCode: number;\n if (!BUILD_DEFS.DISABLE_WEBGPU && ioBindingState) {\n errorCode = await wasm._OrtRunWithBinding(\n sessionHandle, ioBindingState.handle, outputCount, outputValuesOffset, runOptionsHandle);\n } else {\n errorCode = await wasm._OrtRun(\n sessionHandle, inputNamesOffset, inputValuesOffset, inputCount, outputNamesOffset, outputCount,\n outputValuesOffset, runOptionsHandle);\n }\n\n if (errorCode !== 0) {\n checkLastError('failed to call OrtRun().');\n }\n\n const output: TensorMetadata[] = [];\n\n for (let i = 0; i < outputCount; i++) {\n const tensor = wasm.HEAPU32[outputValuesOffset / 4 + i];\n if (tensor === outputTensorHandles[i]) {\n // output tensor is pre-allocated. no need to copy data.\n output.push(outputTensors[i]!);\n continue;\n }\n\n const beforeGetTensorDataStack = wasm.stackSave();\n // stack allocate 4 pointer value\n const tensorDataOffset = wasm.stackAlloc(4 * 4);\n\n let keepOutputTensor = false;\n let type: Tensor.Type|undefined, dataOffset = 0;\n try {\n const errorCode = wasm._OrtGetTensorData(\n tensor, tensorDataOffset, tensorDataOffset + 4, tensorDataOffset + 8, tensorDataOffset + 12);\n if (errorCode !== 0) {\n checkLastError(`Can't access output tensor data on index ${i}.`);\n }\n let tensorDataIndex = tensorDataOffset / 4;\n const dataType = wasm.HEAPU32[tensorDataIndex++];\n dataOffset = wasm.HEAPU32[tensorDataIndex++];\n const dimsOffset = wasm.HEAPU32[tensorDataIndex++];\n const dimsLength = wasm.HEAPU32[tensorDataIndex++];\n const dims = [];\n for (let i = 0; i < dimsLength; i++) {\n dims.push(wasm.HEAPU32[dimsOffset / 4 + i]);\n }\n wasm._OrtFree(dimsOffset);\n\n const size = dims.reduce((a, b) => a * b, 1);\n type = tensorDataTypeEnumToString(dataType);\n\n const preferredLocation = ioBindingState?.outputPreferredLocations[outputIndices[i]];\n\n if (type === 'string') {\n if (preferredLocation === 'gpu-buffer') {\n throw new Error('String tensor is not supported on GPU.');\n }\n const stringData: string[] = [];\n let dataIndex = dataOffset / 4;\n for (let i = 0; i < size; i++) {\n const offset = wasm.HEAPU32[dataIndex++];\n const maxBytesToRead = i === size - 1 ? undefined : wasm.HEAPU32[dataIndex] - offset;\n stringData.push(wasm.UTF8ToString(offset, maxBytesToRead));\n }\n output.push([type, dims, stringData, 'cpu']);\n } else {\n // If a certain output's preferred location is GPU but the tensor is empty, we still need to create a CPU\n // tensor for it. There is no mapping GPU buffer for an empty tensor.\n if (preferredLocation === 'gpu-buffer' && size > 0) {\n const getBuffer = wasm.jsepGetBuffer;\n if (!getBuffer) {\n throw new Error('preferredLocation \"gpu-buffer\" is not supported without using WebGPU.');\n }\n const gpuBuffer = getBuffer(dataOffset);\n const elementSize = getTensorElementSize(dataType);\n if (elementSize === undefined || !isGpuBufferSupportedType(type)) {\n throw new Error(`Unsupported data type: ${type}`);\n }\n\n // do not release the tensor right now. it will be released when user calls tensor.dispose().\n keepOutputTensor = true;\n\n output.push([\n type, dims, {\n gpuBuffer,\n download: wasm.jsepCreateDownloader!(gpuBuffer, size * elementSize, type),\n dispose: () => {\n wasm._OrtReleaseTensor(tensor);\n }\n },\n 'gpu-buffer'\n ]);\n } else {\n const typedArrayConstructor = tensorTypeToTypedArrayConstructor(type);\n const data = new typedArrayConstructor(size);\n new Uint8Array(data.buffer, data.byteOffset, data.byteLength)\n .set(wasm.HEAPU8.subarray(dataOffset, dataOffset + data.byteLength));\n output.push([type, dims, data, 'cpu']);\n }\n }\n } finally {\n wasm.stackRestore(beforeGetTensorDataStack);\n if (type === 'string' && dataOffset) {\n wasm._free(dataOffset);\n }\n if (!keepOutputTensor) {\n wasm._OrtReleaseTensor(tensor);\n }\n }\n }\n\n if (ioBindingState && !enableGraphCapture) {\n wasm._OrtClearBoundOutputs(ioBindingState.handle);\n activeSessions.set(\n sessionId,\n [sessionHandle, inputNamesUTF8Encoded, outputNamesUTF8Encoded, ioBindingState, enableGraphCapture, false]);\n }\n return output;\n } finally {\n wasm.stackRestore(beforeRunStack);\n\n inputTensorHandles.forEach(v => wasm._OrtReleaseTensor(v));\n outputTensorHandles.forEach(v => wasm._OrtReleaseTensor(v));\n inputOutputAllocs.forEach(p => wasm._free(p));\n\n if (runOptionsHandle !== 0) {\n wasm._OrtReleaseRunOptions(runOptionsHandle);\n }\n runOptionsAllocs.forEach(p => wasm._free(p));\n }\n};\n\n/**\n * end profiling\n */\nexport const endProfiling = (sessionId: number): void => {\n const wasm = getInstance();\n const session = activeSessions.get(sessionId);\n if (!session) {\n throw new Error('invalid session id');\n }\n const sessionHandle = session[0];\n\n // profile file name is not used yet, but it must be freed.\n const profileFileName = wasm._OrtEndProfiling(sessionHandle);\n if (profileFileName === 0) {\n checkLastError('Can\\'t get an profile file name.');\n }\n wasm._OrtFree(profileFileName);\n};\n\nexport const extractTransferableBuffers = (tensors: readonly SerializableTensorMetadata[]): ArrayBufferLike[] => {\n const buffers: ArrayBufferLike[] = [];\n for (const tensor of tensors) {\n const data = tensor[2];\n if (!Array.isArray(data) && 'buffer' in data) {\n buffers.push(data.buffer);\n }\n }\n return buffers;\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env, InferenceSession} from 'onnxruntime-common';\n\nimport {OrtWasmMessage, SerializableInternalBuffer, SerializableSessionMetadata, SerializableTensorMetadata, TensorMetadata} from './proxy-messages';\nimport * as core from './wasm-core-impl';\nimport {initializeWebAssembly} from './wasm-factory';\n\nconst isProxy = (): boolean => !!env.wasm.proxy && typeof document !== 'undefined';\nlet proxyWorker: Worker|undefined;\nlet initializing = false;\nlet initialized = false;\nlet aborted = false;\n\ntype PromiseCallbacks = [resolve: (result: T) => void, reject: (reason: unknown) => void];\nlet initWasmCallbacks: PromiseCallbacks;\nconst queuedCallbacks: Map>> = new Map();\n\nconst enqueueCallbacks = (type: OrtWasmMessage['type'], callbacks: PromiseCallbacks): void => {\n const queue = queuedCallbacks.get(type);\n if (queue) {\n queue.push(callbacks);\n } else {\n queuedCallbacks.set(type, [callbacks]);\n }\n};\n\nconst ensureWorker = (): void => {\n if (initializing || !initialized || aborted || !proxyWorker) {\n throw new Error('worker not ready');\n }\n};\n\nconst onProxyWorkerMessage = (ev: MessageEvent): void => {\n switch (ev.data.type) {\n case 'init-wasm':\n initializing = false;\n if (ev.data.err) {\n aborted = true;\n initWasmCallbacks[1](ev.data.err);\n } else {\n initialized = true;\n initWasmCallbacks[0]();\n }\n break;\n case 'init-ep':\n case 'copy-from':\n case 'create':\n case 'release':\n case 'run':\n case 'end-profiling': {\n const callbacks = queuedCallbacks.get(ev.data.type)!;\n if (ev.data.err) {\n callbacks.shift()![1](ev.data.err);\n } else {\n callbacks.shift()![0](ev.data.out!);\n }\n break;\n }\n default:\n }\n};\n\nconst scriptSrc = typeof document !== 'undefined' ? (document?.currentScript as HTMLScriptElement)?.src : undefined;\n\nexport const initializeWebAssemblyAndOrtRuntime = async(): Promise => {\n if (initialized) {\n return;\n }\n if (initializing) {\n throw new Error('multiple calls to \\'initWasm()\\' detected.');\n }\n if (aborted) {\n throw new Error('previous call to \\'initWasm()\\' failed.');\n }\n\n initializing = true;\n\n if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n // overwrite wasm filepaths\n if (env.wasm.wasmPaths === undefined) {\n if (scriptSrc && scriptSrc.indexOf('blob:') !== 0) {\n env.wasm.wasmPaths = scriptSrc.substr(0, +(scriptSrc).lastIndexOf('/') + 1);\n }\n }\n\n return new Promise((resolve, reject) => {\n proxyWorker?.terminate();\n\n const workerUrl = URL.createObjectURL(new Blob(\n [\n // This require() function is handled by esbuild plugin to load file content as string.\n // eslint-disable-next-line @typescript-eslint/no-require-imports\n require('./proxy-worker/main')\n ],\n {type: 'text/javascript'}));\n proxyWorker = new Worker(workerUrl, {name: 'ort-wasm-proxy-worker'});\n proxyWorker.onerror = (ev: ErrorEvent) => reject(ev);\n proxyWorker.onmessage = onProxyWorkerMessage;\n URL.revokeObjectURL(workerUrl);\n initWasmCallbacks = [resolve, reject];\n const message: OrtWasmMessage = {type: 'init-wasm', in : env};\n proxyWorker.postMessage(message);\n });\n\n } else {\n try {\n await initializeWebAssembly(env.wasm);\n await core.initRuntime(env);\n initialized = true;\n } catch (e) {\n aborted = true;\n throw e;\n } finally {\n initializing = false;\n }\n }\n};\n\nexport const initializeOrtEp = async(epName: string): Promise => {\n if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n ensureWorker();\n return new Promise((resolve, reject) => {\n enqueueCallbacks('init-ep', [resolve, reject]);\n const message: OrtWasmMessage = {type: 'init-ep', in : {epName, env}};\n proxyWorker!.postMessage(message);\n });\n } else {\n await core.initEp(env, epName);\n }\n};\n\nexport const copyFromExternalBuffer = async(buffer: Uint8Array): Promise => {\n if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n ensureWorker();\n return new Promise((resolve, reject) => {\n enqueueCallbacks('copy-from', [resolve, reject]);\n const message: OrtWasmMessage = {type: 'copy-from', in : {buffer}};\n proxyWorker!.postMessage(message, [buffer.buffer]);\n });\n } else {\n return core.copyFromExternalBuffer(buffer);\n }\n};\n\nexport const createSession =\n async(model: SerializableInternalBuffer|Uint8Array, options?: InferenceSession.SessionOptions):\n Promise => {\n if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n // check unsupported options\n if (options?.preferredOutputLocation) {\n throw new Error('session option \"preferredOutputLocation\" is not supported for proxy.');\n }\n ensureWorker();\n return new Promise((resolve, reject) => {\n enqueueCallbacks('create', [resolve, reject]);\n const message: OrtWasmMessage = {type: 'create', in : {model, options: {...options}}};\n const transferable: Transferable[] = [];\n if (model instanceof Uint8Array) {\n transferable.push(model.buffer);\n }\n proxyWorker!.postMessage(message, transferable);\n });\n } else {\n return core.createSession(model, options);\n }\n };\n\nexport const releaseSession = async(sessionId: number): Promise => {\n if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n ensureWorker();\n return new Promise((resolve, reject) => {\n enqueueCallbacks('release', [resolve, reject]);\n const message: OrtWasmMessage = {type: 'release', in : sessionId};\n proxyWorker!.postMessage(message);\n });\n } else {\n core.releaseSession(sessionId);\n }\n};\n\nexport const run = async(\n sessionId: number, inputIndices: number[], inputs: TensorMetadata[], outputIndices: number[],\n outputs: Array, options: InferenceSession.RunOptions): Promise => {\n if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n // check inputs location\n if (inputs.some(t => t[3] !== 'cpu')) {\n throw new Error('input tensor on GPU is not supported for proxy.');\n }\n // check outputs location\n if (outputs.some(t => t)) {\n throw new Error('pre-allocated output tensor is not supported for proxy.');\n }\n ensureWorker();\n return new Promise((resolve, reject) => {\n enqueueCallbacks('run', [resolve, reject]);\n const serializableInputs = inputs as SerializableTensorMetadata[]; // every input is on CPU.\n const message: OrtWasmMessage =\n {type: 'run', in : {sessionId, inputIndices, inputs: serializableInputs, outputIndices, options}};\n proxyWorker!.postMessage(message, core.extractTransferableBuffers(serializableInputs));\n });\n } else {\n return core.run(sessionId, inputIndices, inputs, outputIndices, outputs, options);\n }\n};\n\nexport const endProfiling = async(sessionId: number): Promise => {\n if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n ensureWorker();\n return new Promise((resolve, reject) => {\n enqueueCallbacks('end-profiling', [resolve, reject]);\n const message: OrtWasmMessage = {type: 'end-profiling', in : sessionId};\n proxyWorker!.postMessage(message);\n });\n } else {\n core.endProfiling(sessionId);\n }\n};\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession, InferenceSessionHandler, SessionHandler, Tensor, TRACE_FUNC_BEGIN, TRACE_FUNC_END} from 'onnxruntime-common';\n\nimport {SerializableInternalBuffer, TensorMetadata} from './proxy-messages';\nimport {copyFromExternalBuffer, createSession, endProfiling, releaseSession, run} from './proxy-wrapper';\nimport {isGpuBufferSupportedType} from './wasm-common';\nimport {loadFile} from './wasm-utils-load-file';\n\nexport const encodeTensorMetadata = (tensor: Tensor, getName: () => string): TensorMetadata => {\n switch (tensor.location) {\n case 'cpu':\n return [tensor.type, tensor.dims, tensor.data, 'cpu'];\n case 'gpu-buffer':\n return [tensor.type, tensor.dims, {gpuBuffer: tensor.gpuBuffer}, 'gpu-buffer'];\n default:\n throw new Error(`invalid data location: ${tensor.location} for ${getName()}`);\n }\n};\n\nexport const decodeTensorMetadata = (tensor: TensorMetadata): Tensor => {\n switch (tensor[3]) {\n case 'cpu':\n return new Tensor(tensor[0], tensor[2], tensor[1]);\n case 'gpu-buffer': {\n const dataType = tensor[0];\n if (!isGpuBufferSupportedType(dataType)) {\n throw new Error(`not supported data type: ${dataType} for deserializing GPU tensor`);\n }\n const {gpuBuffer, download, dispose} = tensor[2];\n return Tensor.fromGpuBuffer(gpuBuffer, {dataType, dims: tensor[1], download, dispose});\n }\n default:\n throw new Error(`invalid data location: ${tensor[3]}`);\n }\n};\n\nexport class OnnxruntimeWebAssemblySessionHandler implements InferenceSessionHandler {\n private sessionId: number;\n\n inputNames: string[];\n outputNames: string[];\n\n async fetchModelAndCopyToWasmMemory(path: string): Promise {\n // fetch model from url and move to wasm heap.\n return copyFromExternalBuffer(await loadFile(path));\n }\n\n async loadModel(pathOrBuffer: string|Uint8Array, options?: InferenceSession.SessionOptions): Promise {\n TRACE_FUNC_BEGIN();\n let model: Parameters[0];\n\n if (typeof pathOrBuffer === 'string') {\n if (typeof process !== 'undefined' && process.versions && process.versions.node) {\n // node\n model = await loadFile(pathOrBuffer);\n } else {\n // browser\n // fetch model and copy to wasm heap.\n model = await this.fetchModelAndCopyToWasmMemory(pathOrBuffer);\n }\n } else {\n model = pathOrBuffer;\n }\n\n [this.sessionId, this.inputNames, this.outputNames] = await createSession(model, options);\n TRACE_FUNC_END();\n }\n\n async dispose(): Promise {\n return releaseSession(this.sessionId);\n }\n\n async run(feeds: SessionHandler.FeedsType, fetches: SessionHandler.FetchesType, options: InferenceSession.RunOptions):\n Promise {\n TRACE_FUNC_BEGIN();\n const inputArray: Tensor[] = [];\n const inputIndices: number[] = [];\n Object.entries(feeds).forEach(kvp => {\n const name = kvp[0];\n const tensor = kvp[1];\n const index = this.inputNames.indexOf(name);\n if (index === -1) {\n throw new Error(`invalid input '${name}'`);\n }\n inputArray.push(tensor);\n inputIndices.push(index);\n });\n\n const outputArray: Array = [];\n const outputIndices: number[] = [];\n Object.entries(fetches).forEach(kvp => {\n const name = kvp[0];\n const tensor = kvp[1];\n const index = this.outputNames.indexOf(name);\n if (index === -1) {\n throw new Error(`invalid output '${name}'`);\n }\n outputArray.push(tensor);\n outputIndices.push(index);\n });\n\n const inputs =\n inputArray.map((t, i) => encodeTensorMetadata(t, () => `input \"${this.inputNames[inputIndices[i]]}\"`));\n const outputs = outputArray.map(\n (t, i) => t ? encodeTensorMetadata(t, () => `output \"${this.outputNames[outputIndices[i]]}\"`) : null);\n\n const results = await run(this.sessionId, inputIndices, inputs, outputIndices, outputs, options);\n\n const resultMap: SessionHandler.ReturnType = {};\n for (let i = 0; i < results.length; i++) {\n resultMap[this.outputNames[outputIndices[i]]] = outputArray[i] ?? decodeTensorMetadata(results[i]);\n }\n TRACE_FUNC_END();\n return resultMap;\n }\n\n startProfiling(): void {\n // TODO: implement profiling\n }\n\n endProfiling(): void {\n void endProfiling(this.sessionId);\n }\n}\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {cpus} from 'node:os';\nimport {Backend, env, InferenceSession, InferenceSessionHandler} from 'onnxruntime-common';\n\nimport {initializeOrtEp, initializeWebAssemblyAndOrtRuntime} from './wasm/proxy-wrapper';\nimport {OnnxruntimeWebAssemblySessionHandler} from './wasm/session-handler-inference';\n\n/**\n * This function initializes all flags for WebAssembly.\n *\n * Those flags are accessible from `ort.env.wasm`. Users are allow to set those flags before the first inference session\n * being created, to override default value.\n */\nexport const initializeFlags = (): void => {\n if (typeof env.wasm.initTimeout !== 'number' || env.wasm.initTimeout < 0) {\n env.wasm.initTimeout = 0;\n }\n\n if (typeof env.wasm.simd !== 'boolean') {\n env.wasm.simd = true;\n }\n\n if (typeof env.wasm.proxy !== 'boolean') {\n env.wasm.proxy = false;\n }\n\n if (typeof env.wasm.trace !== 'boolean') {\n env.wasm.trace = false;\n }\n\n if (typeof env.wasm.numThreads !== 'number' || !Number.isInteger(env.wasm.numThreads) || env.wasm.numThreads <= 0) {\n // Web: when crossOriginIsolated is false, SharedArrayBuffer is not available so WebAssembly threads will not work.\n // Node.js: onnxruntime-web does not support multi-threads in Node.js.\n if ((typeof self !== 'undefined' && !self.crossOriginIsolated) ||\n (typeof process !== 'undefined' && process.versions && process.versions.node)) {\n env.wasm.numThreads = 1;\n }\n const numCpuLogicalCores = typeof navigator === 'undefined' ? cpus().length : navigator.hardwareConcurrency;\n env.wasm.numThreads = Math.min(4, Math.ceil((numCpuLogicalCores || 1) / 2));\n }\n};\n\nexport class OnnxruntimeWebAssemblyBackend implements Backend {\n /**\n * This function initializes the WebAssembly backend.\n *\n * This function will be called only once for each backend name. It will be called the first time when\n * `ort.InferenceSession.create()` is called with a registered backend name.\n *\n * @param backendName - the registered backend name.\n */\n async init(backendName: string): Promise {\n // populate wasm flags\n initializeFlags();\n\n // init wasm\n await initializeWebAssemblyAndOrtRuntime();\n\n // performe EP specific initialization\n await initializeOrtEp(backendName);\n }\n createInferenceSessionHandler(path: string, options?: InferenceSession.SessionOptions):\n Promise;\n createInferenceSessionHandler(buffer: Uint8Array, options?: InferenceSession.SessionOptions):\n Promise;\n async createInferenceSessionHandler(pathOrBuffer: string|Uint8Array, options?: InferenceSession.SessionOptions):\n Promise {\n const handler = new OnnxruntimeWebAssemblySessionHandler();\n await handler.loadModel(pathOrBuffer, options);\n return Promise.resolve(handler);\n }\n}\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {OnnxruntimeWebAssemblyBackend} from './backend-wasm';\nexport const wasmBackend = new OnnxruntimeWebAssemblyBackend();\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/* eslint-disable @typescript-eslint/no-var-requires, @typescript-eslint/no-require-imports */\n// We use \"require\" instead of \"import\" here because import statement must be put in top level. Our current code does\n// not allow bundler to tree-shaking code as expected because some codes are treated as having side effects.\n// So we import code inside the if-clause to allow bundler remove the code safely.\n\nexport * from 'onnxruntime-common';\nimport * as ort from 'onnxruntime-common';\nexport default ort;\n\nimport {registerBackend, env} from 'onnxruntime-common';\nimport {version} from './version';\n\nif (!BUILD_DEFS.DISABLE_WEBGL) {\n const onnxjsBackend = require('./backend-onnxjs').onnxjsBackend;\n registerBackend('webgl', onnxjsBackend, -10);\n}\n\nif (!BUILD_DEFS.DISABLE_WASM) {\n const wasmBackend = BUILD_DEFS.DISABLE_TRAINING ? require('./backend-wasm-inference').wasmBackend :\n require('./backend-wasm-training').wasmBackend;\n if (!BUILD_DEFS.DISABLE_WEBGPU) {\n registerBackend('webgpu', wasmBackend, 5);\n registerBackend('webnn', wasmBackend, 5);\n }\n registerBackend('cpu', wasmBackend, 10);\n registerBackend('wasm', wasmBackend, 10);\n}\n\nObject.defineProperty(env.versions, 'web', {value: version, enumerable: true});\n", "// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n// This file is generated by /js/scripts/update-version.ts\n// Do not modify file content manually.\n\nexport const version = '1.18.0';\n"], + "mappings": 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