onnxruntime-web
Version:
A Javascript library for running ONNX models on browsers
195 lines (194 loc) • 7.79 kB
JavaScript
'use strict';
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
Object.defineProperty(exports, '__esModule', { value: true });
exports.Session = void 0;
const backend_1 = require('./backend');
const execution_plan_1 = require('./execution-plan');
const instrument_1 = require('./instrument');
const model_1 = require('./model');
class Session {
constructor(config = {}) {
this._initialized = false;
this.backendHint = config.backendHint;
this.profiler = instrument_1.Profiler.create(config.profiler);
this.context = { profiler: this.profiler, graphInputTypes: [], graphInputDims: [] };
}
get inputNames() {
return this._model.graph.getInputNames();
}
get outputNames() {
return this._model.graph.getOutputNames();
}
startProfiling() {
this.profiler.start();
}
endProfiling() {
this.profiler.stop();
}
async loadModel(arg, byteOffset, length) {
await this.profiler.event('session', 'Session.loadModel', async () => {
// resolve backend and session handler
const backend = await (0, backend_1.resolveBackend)(this.backendHint);
this.sessionHandler = backend.createSessionHandler(this.context);
this._model = new model_1.Model();
if (typeof arg === 'string') {
const isOrtFormat = arg.endsWith('.ort');
if (typeof process !== 'undefined' && process.versions && process.versions.node) {
// node
const { readFile } = require('node:fs/promises');
const buf = await readFile(arg);
this.initialize(buf, isOrtFormat);
} else {
// browser
const response = await fetch(arg);
const buf = await response.arrayBuffer();
this.initialize(new Uint8Array(buf), isOrtFormat);
}
} else if (!ArrayBuffer.isView(arg)) {
// load model from ArrayBuffer
const arr = new Uint8Array(arg, byteOffset || 0, length || arg.byteLength);
this.initialize(arr);
} else {
// load model from Uint8array
this.initialize(arg);
}
});
}
initialize(modelProtoBlob, isOrtFormat) {
if (this._initialized) {
throw new Error('already initialized');
}
this.profiler.event('session', 'Session.initialize', () => {
// load graph
const graphInitializer = this.sessionHandler.transformGraph ? this.sessionHandler : undefined;
this._model.load(modelProtoBlob, graphInitializer, isOrtFormat);
// graph is completely initialzied at this stage , let the interested handlers know
if (this.sessionHandler.onGraphInitialized) {
this.sessionHandler.onGraphInitialized(this._model.graph);
}
// initialize each operator in the graph
this.initializeOps(this._model.graph);
// instantiate an ExecutionPlan object to be used by the Session object
this._executionPlan = new execution_plan_1.ExecutionPlan(this._model.graph, this._ops, this.profiler);
});
this._initialized = true;
}
async run(inputs) {
if (!this._initialized) {
throw new Error('session not initialized yet');
}
return this.profiler.event('session', 'Session.run', async () => {
const inputTensors = this.normalizeAndValidateInputs(inputs);
const outputTensors = await this._executionPlan.execute(this.sessionHandler, inputTensors);
return this.createOutput(outputTensors);
});
}
normalizeAndValidateInputs(inputs) {
const modelInputNames = this._model.graph.getInputNames();
// normalize inputs
// inputs: Tensor[]
if (Array.isArray(inputs)) {
if (inputs.length !== modelInputNames.length) {
throw new Error(`incorrect input array length: expected ${modelInputNames.length} but got ${inputs.length}`);
}
}
// convert map to array
// inputs: Map<string, Tensor>
else {
if (inputs.size !== modelInputNames.length) {
throw new Error(`incorrect input map size: expected ${modelInputNames.length} but got ${inputs.size}`);
}
const sortedInputs = new Array(inputs.size);
let sortedInputsIndex = 0;
for (let i = 0; i < modelInputNames.length; ++i) {
const tensor = inputs.get(modelInputNames[i]);
if (!tensor) {
throw new Error(`missing input tensor for: '${name}'`);
}
sortedInputs[sortedInputsIndex++] = tensor;
}
inputs = sortedInputs;
}
// validate dims requirements
// First session run - graph input data is not cached for the session
if (
!this.context.graphInputTypes ||
this.context.graphInputTypes.length === 0 ||
!this.context.graphInputDims ||
this.context.graphInputDims.length === 0
) {
const modelInputIndices = this._model.graph.getInputIndices();
const modelValues = this._model.graph.getValues();
const graphInputDims = new Array(modelInputIndices.length);
for (let i = 0; i < modelInputIndices.length; ++i) {
const graphInput = modelValues[modelInputIndices[i]];
graphInputDims[i] = graphInput.type.shape.dims;
// cached for second and subsequent runs.
// Some parts of the framework works on the assumption that the graph and types and shapes are static
this.context.graphInputTypes.push(graphInput.type.tensorType);
this.context.graphInputDims.push(inputs[i].dims);
}
this.validateInputTensorDims(graphInputDims, inputs, true);
}
// Second and subsequent session runs - graph input data is cached for the session
else {
this.validateInputTensorDims(this.context.graphInputDims, inputs, false);
}
// validate types requirement
this.validateInputTensorTypes(this.context.graphInputTypes, inputs);
return inputs;
}
validateInputTensorTypes(graphInputTypes, givenInputs) {
for (let i = 0; i < givenInputs.length; i++) {
const expectedType = graphInputTypes[i];
const actualType = givenInputs[i].type;
if (expectedType !== actualType) {
throw new Error(`input tensor[${i}] check failed: expected type '${expectedType}' but got ${actualType}`);
}
}
}
validateInputTensorDims(graphInputDims, givenInputs, noneDimSupported) {
for (let i = 0; i < givenInputs.length; i++) {
const expectedDims = graphInputDims[i];
const actualDims = givenInputs[i].dims;
if (!this.compareTensorDims(expectedDims, actualDims, noneDimSupported)) {
throw new Error(
`input tensor[${i}] check failed: expected shape '[${expectedDims.join(',')}]' but got [${actualDims.join(',')}]`,
);
}
}
}
compareTensorDims(expectedDims, actualDims, noneDimSupported) {
if (expectedDims.length !== actualDims.length) {
return false;
}
for (let i = 0; i < expectedDims.length; ++i) {
if (expectedDims[i] !== actualDims[i] && (!noneDimSupported || expectedDims[i] !== 0)) {
// data shape mis-match AND not a 'None' dimension.
return false;
}
}
return true;
}
createOutput(outputTensors) {
const modelOutputNames = this._model.graph.getOutputNames();
if (outputTensors.length !== modelOutputNames.length) {
throw new Error('expected number of outputs do not match number of generated outputs');
}
const output = new Map();
for (let i = 0; i < modelOutputNames.length; ++i) {
output.set(modelOutputNames[i], outputTensors[i]);
}
return output;
}
initializeOps(graph) {
const nodes = graph.getNodes();
this._ops = new Array(nodes.length);
for (let i = 0; i < nodes.length; i++) {
this._ops[i] = this.sessionHandler.resolve(nodes[i], this._model.opsets, graph);
}
}
}
exports.Session = Session;
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