models/closerlook_classifier.py

import torch.nn as nn
import torch
from torch.nn.utils.weight_norm import WeightNorm


class distLinear(nn.Module):
    def __init__(self, indim, outdim):
        super(distLinear, self).__init__()
        self.L = nn.Linear( indim, outdim, bias = False)
        self.class_wise_learnable_norm = True  #See the issue#4&8 in the github
        if self.class_wise_learnable_norm:
            WeightNorm.apply(self.L, 'weight', dim=0) #split the weight update component to direction and norm

        if outdim <=200:
            self.scale_factor = 2; #a fixed scale factor to scale the output of cos value into a reasonably large input for softmax, for to reproduce the result of CUB with ResNet10, use 4. see the issue#31 in the github
        else:
            self.scale_factor = 10; #in omniglot, a larger scale factor is required to handle >1000 output classes.

    def forward(self, x):
        x_norm = torch.norm(x, p=2, dim =1).unsqueeze(1).expand_as(x)
        x_normalized = x.div(x_norm+ 0.00001)
        if not self.class_wise_learnable_norm:
            L_norm = torch.norm(self.L.weight.data, p=2, dim =1).unsqueeze(1).expand_as(self.L.weight.data)
            self.L.weight.data = self.L.weight.data.div(L_norm + 0.00001)
        cos_dist = self.L(x_normalized) #matrix product by forward function, but when using WeightNorm, this also multiply the cosine distance by a class-wise learnable norm, see the issue#4&8 in the github
        scores = self.scale_factor* (cos_dist)

        return scores