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@hoff97/tensor-js

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PyTorch like deep learning inferrence library

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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) { function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } return new (P || (P = Promise))(function (resolve, reject) { function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } step((generator = generator.apply(thisArg, _arguments || [])).next()); }); }; import { OnnxNode } from '../../node'; export class ConcatNode extends OnnxNode { constructor(attributes, inputs, outputs, constants, onnxVersion, mode) { super(attributes, inputs, outputs, constants, onnxVersion, mode); if (onnxVersion < 13) { //@ts-ignore this.axis = this.getAttributeInt('axis'); } } forward(inputs) { return __awaiter(this, void 0, void 0, function* () { if (inputs.length > 2) { // This logging seems to slow down the operation more than the operation itself //console.warn(`Concat with more than 2 tensors is currently slow. Doing concat with ${inputs.length} tensors`); } if (this.onnxVersion < 13 && this.axis !== undefined) { let result = inputs[0]; for (let i = 1; i < inputs.length; i++) { const newRes = result.concat(inputs[i], this.axis); if (i > 1) { result.delete(); } result = newRes; } return [result]; } throw new Error(`Concat not implemented for onnx version ${this.onnxVersion}`); }); } getType() { return 'Concat'; } delete() { } } //# sourceMappingURL=concat.js.map