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import torch
from torch import nn
import torch.nn.functional as F
class BoundaryLoss(nn.Module):
"""
Deep Open Intent Classification with Adaptive Decision Boundary.
https://arxiv.org/pdf/2012.10209.pdf
"""
def __init__(self, num_labels=10, feat_dim=2, device = None):
super(BoundaryLoss, self).__init__()
self.num_labels = num_labels
self.feat_dim = feat_dim
self.delta = nn.Parameter(torch.randn(num_labels).to(device))
nn.init.normal_(self.delta)
def forward(self, pooled_output, centroids, labels):
delta = F.softplus(self.delta)
c = centroids[labels]
d = delta[labels]
x = pooled_output
euc_dis = torch.norm(x - c,2, 1).view(-1)
pos_mask = (euc_dis > d).type(torch.cuda.FloatTensor)
neg_mask = (euc_dis < d).type(torch.cuda.FloatTensor)
pos_loss = (euc_dis - d) * pos_mask
neg_loss = (d - euc_dis) * neg_mask
loss = pos_loss.mean() + neg_loss.mean()
return loss, delta |