@hoff97/tensor-js
Version:
PyTorch like deep learning inferrence library
38 lines • 1.78 kB
JavaScript
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 { CPUTensor } from '../../tensor/cpu/tensor';
import { OnnxNode } from '../node';
export class ExpandNode extends OnnxNode {
constructor(attributes, inputs, outputs, constants, onnxVersion, mode) {
super(attributes, inputs, outputs, constants, onnxVersion, mode);
}
forward(inputs) {
return __awaiter(this, void 0, void 0, function* () {
if (this.onnxVersion < 13) {
const tensor = inputs[0];
const _shape = inputs[1];
if (!(_shape instanceof CPUTensor)) {
throw new Error('Expand needs cpu tensor as shape tensor');
}
const shape = new Array(_shape.size);
for (let i = 0; i < _shape.size; i++) {
shape[i] = _shape.get(i);
}
return [tensor.expand(shape)];
}
throw new Error(`Expand not yet implemented for onnx version ${this.onnxVersion}`);
});
}
getType() {
return 'Expand';
}
delete() { }
}
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