@hoff97/tensor-js
Version:
PyTorch like deep learning inferrence library
34 lines • 1.51 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 { OnnxNode } from '../node';
export class PReluNode 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* () {
const tensor = inputs[0];
const slope = inputs[1];
const below = tensor.clip(undefined, 0);
const above = tensor.clip(0, undefined);
const belowRes = below.multiply(slope);
below.delete();
const result = belowRes.add(above);
belowRes.delete();
above.delete();
return [result];
});
}
delete() { }
getType() {
return 'PRElu';
}
}
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