normalized_linear.py

import math

import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.parameter import Parameter
import torch.nn.functional as F


class NormalizedLinear(nn.Module):
    r"""Normalized linear layer which was proposed by Jiaqi Wang et. al. in 
        their technical report for LVIS workshop at ECCV 2020.
        https://arxiv.org/pdf/2008.10032.pdf
    Args:
        in_features: size of each input sample
        out_features: size of each output sample
        bias: If set to ``False``, the layer will not learn an additive bias.
            Default: ``True``
        tau: a temperature factor, set as 20 by default by following the paper.
    """
    __constants__ = ['in_features', 'out_features']
    in_features: int
    out_features: int
    weight: Tensor

    def __init__(self, in_features: int, out_features: int, bias: bool = True, tau: float = 20) -> None:
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(out_features, in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()
        self.tau = tau

    def reset_parameters(self) -> None:
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            nn.init.uniform_(self.bias, -bound, bound)

    def forward(self, input: Tensor) -> Tensor:

        return F.linear(
            input / torch.norm(input, dim=-1, keepdim=True),
            self.tau * self.weight /
            torch.norm(self.weight, dim=-1, keepdim=True),
            self.bias
        )

    def extra_repr(self) -> str:
        return 'in_features={}, out_features={}, bias={}'.format(
            self.in_features, self.out_features, self.bias is not None
        )