Normalize.py

from torch import Tensor
from torch import nn
from typing import Dict
import torch.nn.functional as F


class Normalize(nn.Module):
    """
    This layer normalizes embeddings to unit length
    """

    def __init__(self):
        super(Normalize, self).__init__()

    def forward(self, features: Dict[str, Tensor]):
        features.update({"sentence_embedding": F.normalize(features["sentence_embedding"], p=2, dim=1)})
        return features

    def save(self, output_path):
        pass

    @staticmethod
    def load(input_path):
        return Normalize()