chore: update node_modules with new binary files and dependencies
- Add new binary files for nodemon, onnxruntime-web, and xenova/transformers - Update various JavaScript and TypeScript files in node_modules - Remove unused files and dependencies - Add new test fixtures and documentation files
This commit is contained in:
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640
node_modules/onnxruntime-web/lib/onnxjs/graph.js
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node_modules/onnxruntime-web/lib/onnxjs/graph.js
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"use strict";
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.Graph = void 0;
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const onnx_proto_1 = require("onnx-proto");
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const attribute_1 = require("./attribute");
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const ort_generated_1 = require("./ort-schema/ort-generated");
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const tensor_1 = require("./tensor");
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const util_1 = require("./util");
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var ortFbs = ort_generated_1.onnxruntime.experimental.fbs;
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// eslint-disable-next-line @typescript-eslint/naming-convention, @typescript-eslint/no-redeclare
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exports.Graph = {
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/**
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* construct a graph from a graph protobuf type
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*/
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from: (graphProto, initializer) => new GraphImpl(graphProto, initializer),
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};
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class Value {
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constructor(valueInfo) {
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this._from = undefined;
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this._to = [];
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this.tensor = undefined;
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this.type = undefined;
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if (valueInfo) {
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this.type = util_1.ProtoUtil.tensorValueTypeFromProto(valueInfo.type.tensorType);
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}
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}
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get from() {
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return this._from;
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}
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get to() {
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return this._to;
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}
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}
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class Node {
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constructor(_nodeProto, name) {
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if (_nodeProto instanceof onnx_proto_1.onnx.NodeProto) {
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this.name = _nodeProto.name;
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this.opType = _nodeProto.opType;
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this.attributes = new attribute_1.Attribute(_nodeProto.attribute);
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}
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else if (_nodeProto instanceof ortFbs.Node) {
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this.name = name !== null && name !== void 0 ? name : _nodeProto.name();
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this.opType = _nodeProto.opType();
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this.attributes = new attribute_1.Attribute(util_1.ProtoUtil.tensorAttributesFromORTFormat(_nodeProto));
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}
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this.inputs = [];
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this.outputs = [];
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this.executeNode = true;
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}
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}
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class GraphImpl {
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constructor(graph, graphInitializer) {
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if (!graph) {
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throw new TypeError('graph is empty');
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}
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// build the graph - will throw exceptions if something fatal is detected
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this.buildGraph(graph);
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// execute any transformation logic for the graph (if applicable)
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this.transformGraph(graphInitializer);
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// check for cycles and other inconsistencies - will throw exceptions if something fatal is detected
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this.checkIsAcyclic();
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}
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getInputIndices() {
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return this._allInputIndices;
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}
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getInputNames() {
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return this._allInputNames;
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}
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getOutputIndices() {
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return this._allOutputIndices;
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}
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getOutputNames() {
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return this._allOutputNames;
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}
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getValues() {
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return this._allData;
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}
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getNodes() {
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return this._nodes;
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}
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buildGraph(graph) {
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// build the graph - will throw exceptions if something fatal is detected
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if (graph instanceof onnx_proto_1.onnx.GraphProto) {
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this.buildGraphFromOnnxFormat(graph);
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}
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else if (graph instanceof ortFbs.Graph) {
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this.buildGraphFromOrtFormat(graph);
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}
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else {
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throw new TypeError('Graph type is not supported.');
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}
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}
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buildGraphFromOnnxFormat(graph) {
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const dataIndices = new Map();
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this._allData = [];
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this._allInputIndices = [];
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this._allInputNames = [];
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this._allOutputIndices = [];
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this._allOutputNames = [];
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this._nodes = [];
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const nodesIndices = new Map();
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// scan all inputs
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if (!graph.input) {
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throw new Error('missing information in graph: input');
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}
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const inputValueNames = [];
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for (const i of graph.input) {
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if (dataIndices.has(i.name)) {
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throw new Error(`duplicated input name: ${i.name}`);
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}
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const currentIndex = this._allData.push(new Value(i)) - 1;
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dataIndices.set(i.name, currentIndex);
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inputValueNames.push(i.name);
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}
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// scan all initializers
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if (!graph.initializer) {
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throw new Error('missing information in graph: initializer');
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}
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for (const i of graph.initializer) {
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let index = dataIndices.get(i.name);
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if (index === undefined) {
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const value = new Value();
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value.type = {
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shape: { dims: util_1.ProtoUtil.tensorDimsFromProto(i.dims) },
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tensorType: util_1.ProtoUtil.tensorDataTypeFromProto(i.dataType)
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};
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index = this._allData.push(value) - 1;
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dataIndices.set(i.name, index);
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}
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this._allData[index]._from = -1;
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this._allData[index].tensor = tensor_1.Tensor.fromProto(i);
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}
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// filter out input indices
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for (let i = 0; i < this._allData.length; i++) {
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if (!this._allData[i].tensor) {
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this._allInputIndices.push(i);
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this._allInputNames.push(inputValueNames[i]);
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}
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}
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// scan all outputs
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if (!graph.output) {
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throw new Error('missing information in graph: output');
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}
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for (const i of graph.output) {
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if (dataIndices.has(i.name)) {
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throw new Error(`duplicated output name: ${i.name}`);
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}
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const currentIndex = this._allData.push(new Value(i)) - 1;
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dataIndices.set(i.name, currentIndex);
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this._allOutputIndices.push(currentIndex);
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this._allOutputNames.push(i.name);
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}
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// scan all nodes
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if (!graph.node) {
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throw new Error('missing information in graph: node');
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}
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for (const nodeProto of graph.node) {
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if (!nodeProto.name) {
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// assign a name to the node if it doesn't have one
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for (let pick = 0;; pick++) {
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const name = `unnamed_${nodeProto.opType}_${pick}`;
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if (!nodesIndices.has(name)) {
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nodeProto.name = name;
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break;
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}
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}
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}
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if (nodesIndices.has(nodeProto.name)) {
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throw new Error(`duplicated node name: ${nodeProto.name}`);
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}
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const currentIndex = this._nodes.push(new Node(nodeProto)) - 1;
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nodesIndices.set(nodeProto.name, currentIndex);
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}
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// scan node's outputs
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for (let i = 0; i < this._nodes.length; i++) {
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const node = this._nodes[i];
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const nodeProto = graph.node[i];
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if (!nodeProto.output) {
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throw new Error(`missing output for node: ${nodeProto.name}`);
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}
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for (const output of nodeProto.output) {
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let dataIndex = dataIndices.get(output);
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if (typeof dataIndex === 'undefined') {
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dataIndex = this._allData.push(new Value()) - 1;
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dataIndices.set(output, dataIndex);
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}
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node.outputs.push(dataIndex);
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if (this._allData[dataIndex]._from !== undefined) {
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throw new Error(`multiple nodes output to one data value: ${dataIndex}`);
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}
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this._allData[dataIndex]._from = i;
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// for the 'Constant' operator, just create a new edge in the graph corresponding to the 'output' of the
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// operator and ignore the node from the graph
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if (nodeProto.opType === 'Constant') {
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if (!nodeProto.attribute || nodeProto.attribute.length !== 1 || !nodeProto.attribute[0].t) {
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throw new Error('missing attributes or missing tensor value in attributes for this Constant operator');
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}
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if (!nodeProto.output || nodeProto.output.length !== 1) {
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throw new Error('missing output or incorrect number of outputs for this Constant operator');
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}
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node.outputs.pop();
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node.executeNode = false;
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this._allData[dataIndex]._from = -1;
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this._allData[dataIndex].tensor = tensor_1.Tensor.fromProto(nodeProto.attribute[0].t);
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}
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}
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}
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// scan node's inputs
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for (let i = 0; i < this._nodes.length; i++) {
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const node = this._nodes[i];
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const nodeProto = graph.node[i];
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if (!nodeProto.input) {
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throw new Error(`missing input for node: ${nodeProto.name}`);
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}
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for (const input of nodeProto.input) {
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const dataIndex = dataIndices.get(input);
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if (typeof dataIndex === 'undefined') {
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// handle exception when opset > 9 and roi not given
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if (input === '' && nodeProto.input.length === 3 && nodeProto.opType === 'Resize') {
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continue;
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}
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throw new Error(`unrecognized input '${input}' for node: ${nodeProto.name}`);
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}
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node.inputs.push(dataIndex);
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this._allData[dataIndex]._to.push(i);
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}
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}
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return true;
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}
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buildGraphFromOrtFormat(graph) {
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var _a, _b, _c;
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const dataIndices = new Map();
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this._allData = [];
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this._allInputIndices = [];
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this._allInputNames = [];
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this._allOutputIndices = [];
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this._allOutputNames = [];
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this._nodes = [];
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const nodesIndices = new Map();
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// scan all inputs
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const inputValueNames = [];
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for (let i = 0; i < graph.inputsLength(); i++) {
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const inputName = graph.inputs(i);
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if (dataIndices.has(inputName)) {
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throw new Error(`duplicated input name: ${inputName}`);
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}
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// Find the input typeInfo from nodeargs
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for (let j = 0; j < graph.nodeArgsLength(); j++) {
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if (((_a = graph.nodeArgs(j)) === null || _a === void 0 ? void 0 : _a.name()) === inputName) {
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const value = new Value();
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const valueType = (_c = (_b = graph.nodeArgs(j)) === null || _b === void 0 ? void 0 : _b.type()) === null || _c === void 0 ? void 0 : _c.valueType();
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if (valueType !== ortFbs.TypeInfoValue.tensor_type) {
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throw new Error('Unexpected value type for the nodeArg.');
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}
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const valueInfo = graph.nodeArgs(j).type().value(new ortFbs.TensorTypeAndShape());
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const type = util_1.ProtoUtil.tensorDataTypeFromProto(valueInfo.elemType());
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const shape = valueInfo.shape();
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const dims = [];
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for (let k = 0; k < shape.dimLength(); k++) {
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dims.push(util_1.LongUtil.longToNumber(shape.dim(k).value().dimValue()));
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}
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value.type = { shape: { dims }, tensorType: type };
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const currentIndex = this._allData.push(value) - 1;
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dataIndices.set(inputName, currentIndex);
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inputValueNames.push(inputName);
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}
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}
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}
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// check initializers
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for (let i = 0; i < graph.initializersLength(); i++) {
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const initializer = graph.initializers(i);
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let index = dataIndices.get(initializer.name());
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if (index === undefined) {
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const value = new Value();
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const dims = util_1.ProtoUtil.tensorDimsFromORTFormat(initializer);
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const type = util_1.ProtoUtil.tensorDataTypeFromProto(initializer.dataType());
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value.type = { shape: { dims }, tensorType: type };
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index = this._allData.push(value) - 1;
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dataIndices.set(initializer.name(), index);
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}
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this._allData[index]._from = -1;
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this._allData[index].tensor = tensor_1.Tensor.fromOrtTensor(initializer);
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}
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// filter out input indices
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for (let i = 0; i < this._allData.length; i++) {
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if (!this._allData[i].tensor) {
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this._allInputIndices.push(i);
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this._allInputNames.push(inputValueNames[i]);
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}
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}
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// scan all outputs
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for (let i = 0; i < graph.outputsLength(); i++) {
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const outputName = graph.outputs(i);
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if (dataIndices.has(outputName)) {
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throw new Error(`duplicated output name: ${outputName}`);
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}
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const currentIndex = this._allData.push(new Value()) - 1;
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dataIndices.set(outputName, currentIndex);
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this._allOutputIndices.push(currentIndex);
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this._allOutputNames.push(outputName);
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}
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// scan all nodes
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if (!graph.nodes) {
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throw new Error('missing information in graph: node');
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}
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for (let i = 0; i < graph.nodesLength(); i++) {
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const nodeProto = graph.nodes(i);
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let name = nodeProto.name();
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if (!name) {
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// assign a name to the node if it doesn't have one
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for (let pick = 0;; pick++) {
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name = `unnamed_${nodeProto.opType()}_${pick}`;
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if (!nodesIndices.has(name)) {
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// an unique name is found. break.
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break;
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}
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}
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}
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if (nodesIndices.has(name)) {
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throw new Error(`duplicated node name: ${name}`);
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}
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const currentIndex = this._nodes.push(new Node(nodeProto, name)) - 1;
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nodesIndices.set(name, currentIndex);
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}
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// scan node's outputs
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for (let i = 0; i < this._nodes.length; i++) {
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const node = this._nodes[i];
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const nodeProto = graph.nodes(i);
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if (nodeProto == null) {
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throw new Error(`No node exists at index ${i}`);
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}
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if ((nodeProto === null || nodeProto === void 0 ? void 0 : nodeProto.outputsLength()) === 0) {
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throw new Error(`missing output for node: ${nodeProto.name}`);
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}
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for (let j = 0; j < (nodeProto === null || nodeProto === void 0 ? void 0 : nodeProto.outputsLength()); j++) {
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const output = nodeProto === null || nodeProto === void 0 ? void 0 : nodeProto.outputs(j);
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let dataIndex = dataIndices.get(output);
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if (typeof dataIndex === 'undefined') {
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dataIndex = this._allData.push(new Value()) - 1;
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dataIndices.set(output, dataIndex);
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}
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node.outputs.push(dataIndex);
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if (this._allData[dataIndex]._from !== undefined) {
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throw new Error(`multiple nodes output to one data value: ${dataIndex}`);
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}
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this._allData[dataIndex]._from = i;
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// for the 'Constant' operator, just create a new edge in the graph corresponding to the 'output' of the
|
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// operator and ignore the node from the graph
|
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if (nodeProto.opType() === 'Constant') {
|
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if (nodeProto.attributesLength() !== 1 || !nodeProto.attributes(0).t()) {
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throw new Error('missing attributes or missing tensor value in attributes for this Constant operator');
|
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}
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if (nodeProto.outputsLength() !== 1) {
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throw new Error('missing output or incorrect number of outputs for this Constant operator');
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}
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node.outputs.pop();
|
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node.executeNode = false;
|
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this._allData[dataIndex]._from = -1;
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this._allData[dataIndex].tensor = tensor_1.Tensor.fromOrtTensor(nodeProto.attributes(0).t());
|
||||
}
|
||||
}
|
||||
}
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||||
// scan node's inputs
|
||||
for (let i = 0; i < this._nodes.length; i++) {
|
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const node = this._nodes[i];
|
||||
const nodeProto = graph.nodes(i);
|
||||
if (nodeProto.inputsLength() === 0) {
|
||||
throw new Error(`missing input for node: ${nodeProto.name}`);
|
||||
}
|
||||
for (let j = 0; j < nodeProto.inputsLength(); j++) {
|
||||
const input = nodeProto.inputs(j);
|
||||
const dataIndex = dataIndices.get(input);
|
||||
if (typeof dataIndex === 'undefined') {
|
||||
throw new Error(`unrecognized input '${input}' for node: ${nodeProto.name()}`);
|
||||
}
|
||||
node.inputs.push(dataIndex);
|
||||
this._allData[dataIndex]._to.push(i);
|
||||
}
|
||||
}
|
||||
}
|
||||
checkIsAcyclic() {
|
||||
// go through the graph and check for cycles or other fatal inconsistencies
|
||||
const starters = new Set();
|
||||
this._allInputIndices.forEach(i => {
|
||||
const data = this._allData[i];
|
||||
data._to.forEach(j => {
|
||||
starters.add(j);
|
||||
});
|
||||
});
|
||||
// Iterative DFS to check for cycles
|
||||
const nodesStack = Array.from(starters);
|
||||
const nodesState = new Array(this._nodes.length).fill('white');
|
||||
while (nodesStack.length > 0) {
|
||||
const nodeIndex = nodesStack.pop();
|
||||
// this node has now been processed completely. Mark this node 'black' to denote this.
|
||||
if (nodesState[nodeIndex] === 'gray') {
|
||||
nodesState[nodeIndex] = 'black';
|
||||
}
|
||||
else {
|
||||
// this node is under processing stage. mark this node 'gray' to denote this.
|
||||
nodesStack.push(nodeIndex);
|
||||
nodesState[nodeIndex] = 'gray';
|
||||
this._nodes[nodeIndex].outputs.forEach((outgoingEdgeIndex) => {
|
||||
const data = this._allData[outgoingEdgeIndex];
|
||||
if (typeof data.tensor !== 'undefined') {
|
||||
throw new Error('node outputs should not be initialized');
|
||||
}
|
||||
if (data._from !== nodeIndex) {
|
||||
throw new Error('from property of the Value object doesn\'t match index of Node being processed');
|
||||
}
|
||||
data._to.forEach((downstreamNodeIndex) => {
|
||||
// back edge found - cyclic
|
||||
if (nodesState[downstreamNodeIndex] === 'gray') {
|
||||
throw new Error('model graph is cyclic');
|
||||
}
|
||||
// tree edge found - continue processing by adding it to stack
|
||||
else if (nodesState[downstreamNodeIndex] === 'white') {
|
||||
nodesStack.push(downstreamNodeIndex);
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
transformGraph(graphInitializer) {
|
||||
// apply common transform
|
||||
this.removeAllIdentityNodes();
|
||||
this.removeAllDropoutNodes();
|
||||
this.fuseConvActivationNodes();
|
||||
// apply initializer specific transform
|
||||
if (graphInitializer) {
|
||||
graphInitializer.transformGraph(this);
|
||||
}
|
||||
// finalize graph
|
||||
this.finalizeGraph();
|
||||
}
|
||||
/**
|
||||
* finalize the graph.
|
||||
*
|
||||
* this function should be called after all the transformation completed.
|
||||
* this function removes all unnecessary nodes and values from the graph
|
||||
*/
|
||||
finalizeGraph() {
|
||||
let offset = 0;
|
||||
// delete all nodes that are not being executed
|
||||
for (let i = 0; i < this._nodes.length; i++) {
|
||||
if (!this._nodes[i].executeNode) {
|
||||
// delete this node and shift all subsequent nodes up
|
||||
offset++;
|
||||
// delete all output values
|
||||
this._nodes[i].outputs.forEach(ind => {
|
||||
this._allData[ind]._from = -2;
|
||||
});
|
||||
this._nodes.splice(i, 1);
|
||||
i--;
|
||||
continue;
|
||||
}
|
||||
if (offset > 0) {
|
||||
// update the value table
|
||||
this._nodes[i].inputs.forEach(value => {
|
||||
const ind = this._allData[value]._to.indexOf(i + offset);
|
||||
if (ind !== -1) {
|
||||
this._allData[value]._to[ind] = i;
|
||||
}
|
||||
});
|
||||
this._nodes[i].outputs.forEach(value => {
|
||||
if (this._allData[value]._from && this._allData[value]._from === i + offset) {
|
||||
this._allData[value]._from = i;
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
offset = 0;
|
||||
// delete all values that are not being referenced
|
||||
for (let i = 0; i < this._allData.length; i++) {
|
||||
// if current value is neither linked to next node, nor an output value, remove it.
|
||||
if (this._allData[i].from === -2 && this._allOutputIndices.indexOf(i + offset) === -1) {
|
||||
offset++;
|
||||
this._allData.splice(i, 1);
|
||||
i--;
|
||||
continue;
|
||||
}
|
||||
if (offset > 0) {
|
||||
let ind = -1;
|
||||
// if current value is neither an input value nor an initializer, find the node it's
|
||||
// coming from and update the corresponding node output
|
||||
if (this._allData[i].from !== undefined && this._allData[i].from !== -1) {
|
||||
ind = this._nodes[this._allData[i].from].outputs.indexOf(i + offset);
|
||||
if (ind !== -1) {
|
||||
this._nodes[this._allData[i].from].outputs[ind] = i;
|
||||
}
|
||||
}
|
||||
else {
|
||||
// if current value is an input value, update its reference in inputIndices
|
||||
ind = this._allInputIndices.indexOf(i + offset);
|
||||
if (ind !== -1) {
|
||||
this._allInputIndices[ind] = i;
|
||||
}
|
||||
}
|
||||
// find the node that the current value is linking to and update its input reference
|
||||
this._allData[i].to.forEach(node => {
|
||||
ind = this._nodes[node].inputs.indexOf(i + offset);
|
||||
if (ind !== -1) {
|
||||
this._nodes[node].inputs[ind] = i;
|
||||
}
|
||||
});
|
||||
if (this._allData[i].to.length === 0) {
|
||||
// if current value is a graph output, update its reference in outputIndices
|
||||
ind = this._allOutputIndices.indexOf(i + offset);
|
||||
if (ind !== -1) {
|
||||
this._allOutputIndices[ind] = i;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Delete the specifed node. Assume the node has one incoming input and the first output connected to other nodes.
|
||||
* An input validation must be done before calling this function.
|
||||
* @param nodeIndex The index of node to be deleted
|
||||
*/
|
||||
deleteNode(nodeIndex) {
|
||||
const node = this._nodes[nodeIndex];
|
||||
if (node.outputs.length > 1) {
|
||||
for (let i = 1; i < node.outputs.length; i++) {
|
||||
if (this._allData[node.outputs[i]].to.length > 0) {
|
||||
throw new Error('Node deletion with more than one output connected to other nodes is not supported. ');
|
||||
}
|
||||
}
|
||||
}
|
||||
// this node wil not be executed
|
||||
node.executeNode = false;
|
||||
const inputValueIndex = node.inputs[0];
|
||||
const outputValueIndex = node.outputs[0];
|
||||
const nodesConsumingOutput = this._allData[outputValueIndex].to;
|
||||
// remove this node from the to property of the input Value
|
||||
const delIndex = this._allData[inputValueIndex].to.indexOf(nodeIndex);
|
||||
// should not happen
|
||||
if (delIndex === -1) {
|
||||
throw new Error('The Value object doesn\'t have the current Node in it\'s \'to\' property ');
|
||||
}
|
||||
this._allData[inputValueIndex].to.splice(delIndex, 1);
|
||||
// clear node indices consuming this output Value
|
||||
this._allData[outputValueIndex]._to = [];
|
||||
// if the output of this node is a graph output, adjust the index appropriately
|
||||
const index = this._allOutputIndices.indexOf(outputValueIndex);
|
||||
if (index !== -1) {
|
||||
this._allOutputIndices[index] = inputValueIndex;
|
||||
}
|
||||
// override the inputs for nodes consuming this node's output with the input to this node
|
||||
if (nodesConsumingOutput && nodesConsumingOutput.length > 0) {
|
||||
for (const nodeIndex of nodesConsumingOutput) {
|
||||
const replaceIndex = this._nodes[nodeIndex].inputs.indexOf(outputValueIndex);
|
||||
// should not happen
|
||||
if (replaceIndex === -1) {
|
||||
throw new Error('The Node object doesn\'t have the output Value in it\'s \'inputs\' property ');
|
||||
}
|
||||
this._nodes[nodeIndex].inputs[replaceIndex] = inputValueIndex;
|
||||
this._allData[inputValueIndex].to.push(nodeIndex);
|
||||
}
|
||||
}
|
||||
}
|
||||
removeAllDropoutNodes() {
|
||||
let nodeIndex = 0;
|
||||
for (const node of this._nodes) {
|
||||
// weed out 'Dropout' nodes so that no time is wasted in execution
|
||||
if (node.opType === 'Dropout') {
|
||||
// the node should have exactly 1 input and 1 or 2 outputs
|
||||
if (node.inputs.length !== 1) {
|
||||
throw new Error('Dropout nodes should only contain one input. ');
|
||||
}
|
||||
if (node.outputs.length !== 1 && node.outputs.length !== 2) {
|
||||
throw new Error('Dropout nodes should contain either 1 or 2 output(s)');
|
||||
}
|
||||
// the second output should not be referenced by any other node
|
||||
if (node.outputs.length === 2 && this._allData[node.outputs[1]]._to.length !== 0) {
|
||||
throw new Error('Dropout nodes\'s second output should not be referenced by other nodes');
|
||||
}
|
||||
this.deleteNode(nodeIndex);
|
||||
}
|
||||
nodeIndex++;
|
||||
}
|
||||
}
|
||||
removeAllIdentityNodes() {
|
||||
let nodeIndex = 0;
|
||||
for (const node of this._nodes) {
|
||||
// weed out 'Identity' nodes so that no time is wasted in execution
|
||||
if (node.opType === 'Identity') {
|
||||
this.deleteNode(nodeIndex);
|
||||
}
|
||||
nodeIndex++;
|
||||
}
|
||||
}
|
||||
isActivation(n) {
|
||||
switch (n.opType) {
|
||||
// TODO: add other activation methods
|
||||
case 'Relu':
|
||||
case 'Sigmoid':
|
||||
case 'Clip':
|
||||
return true;
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
}
|
||||
fuseConvActivationNodes() {
|
||||
for (const node of this._nodes) {
|
||||
if (node.opType === 'Conv') {
|
||||
const next = this._allData[node.outputs[0]]._to;
|
||||
if (next.length === 1 && this.isActivation(this._nodes[next[0]])) {
|
||||
const child = this._nodes[next[0]];
|
||||
if (child.opType === 'Clip') {
|
||||
if (child.inputs.length === 1) {
|
||||
try {
|
||||
node.attributes.set('activation_params', 'floats', [child.attributes.getFloat('min'), child.attributes.getFloat('max')]);
|
||||
}
|
||||
catch (e) {
|
||||
node.attributes.set('activation_params', 'floats', [util_1.MIN_CLIP, util_1.MAX_CLIP]);
|
||||
}
|
||||
}
|
||||
else if (child.inputs.length >= 3 && this._allData[child.inputs[1]].tensor !== undefined &&
|
||||
this._allData[child.inputs[2]].tensor !== undefined) {
|
||||
node.attributes.set('activation_params', 'floats', [
|
||||
this._allData[child.inputs[1]].tensor.floatData[0], this._allData[child.inputs[2]].tensor.floatData[0]
|
||||
]);
|
||||
}
|
||||
else {
|
||||
// Skip fusion with clip node since clip min and clip max are not coming from initializer
|
||||
continue;
|
||||
}
|
||||
}
|
||||
node.attributes.set('activation', 'string', (child.opType));
|
||||
this.deleteNode(next[0]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
//# sourceMappingURL=graph.js.map
|
||||
Reference in New Issue
Block a user