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#!/usr/bin/env python3
# coding=utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.module.biaffine import Biaffine
class AnchorClassifier(nn.Module):
def __init__(self, dataset, args, initialize: bool, bias=True, mode="anchor"):
super(AnchorClassifier, self).__init__()
self.token_f = nn.Linear(args.hidden_size, args.hidden_size_anchor)
self.label_f = nn.Linear(args.hidden_size, args.hidden_size_anchor)
self.dropout = nn.Dropout(args.dropout_anchor)
if bias and initialize:
bias_init = torch.tensor([getattr(dataset, f"{mode}_freq")])
bias_init = (bias_init / (1.0 - bias_init)).log()
else:
bias_init = None
self.output = Biaffine(args.hidden_size_anchor, 1, bias=bias, bias_init=bias_init)
def forward(self, label, tokens, encoder_mask):
tokens = self.dropout(F.elu(self.token_f(tokens))) # shape: (B, T_w, H)
label = self.dropout(F.elu(self.label_f(label))) # shape: (B, T_l, H)
anchor = self.output(label, tokens).squeeze(-1) # shape: (B, T_l, T_w)
anchor = anchor.masked_fill(encoder_mask.unsqueeze(1), float("-inf")) # shape: (B, T_l, T_w)
return anchor
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