@hoff97/tensor-js
Version:
PyTorch like deep learning inferrence library
42 lines • 1.83 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 ClipNode extends OnnxNode {
constructor(attributes, inputs, outputs, constants, onnxVersion, mode) {
super(attributes, inputs, outputs, constants, onnxVersion, mode);
if (onnxVersion < 11) {
//@ts-ignore
this.min = this.getAttributeFloat('min');
//@ts-ignore
this.max = this.getAttributeFloat('max');
}
}
forward(inputs) {
return __awaiter(this, void 0, void 0, function* () {
const x = inputs[0];
if (this.onnxVersion < 11) {
return [x.clip(this.min, this.max)];
}
else {
const min = inputs.length > 1 ? inputs[1] : undefined;
const max = inputs.length > 2 ? inputs[2] : undefined;
if (min === undefined && max === undefined) {
return [x.copy()];
}
throw new Error('Clip with onnx version >= 11 not yet implemented');
}
});
}
getType() {
return 'Clip';
}
delete() { }
}
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