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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 UnsqueezeNode extends OnnxNode { constructor(attributes, inputs, outputs, constants, onnxVersion, mode) { super(attributes, inputs, outputs, constants, onnxVersion, mode); if (onnxVersion < 13) { this.axes = this.getAttributeInts('axes'); } } forward(inputs) { return __awaiter(this, void 0, void 0, function* () { const x = inputs[0]; if (this.onnxVersion < 13 && this.axes !== undefined) { const currShape = x.getShape(); const newShape = []; let axIx = 0; for (let i = 0; i < currShape.length; i++) { if (axIx < this.axes.length && this.axes[axIx] === i) { newShape.push(1); axIx++; } newShape.push(currShape[i]); } if (this.axes[this.axes.length - 1] === currShape.length) { newShape.push(1); } return [x.reshape(newShape)]; } throw new Error(`Unsqueeze with onnx version ${this.onnxVersion} not yet implemented`); }); } getType() { return 'Unsqueeze'; } delete() { } } //# sourceMappingURL=unsqueeze.js.map