| import torch |
| import torch.nn.functional as F |
| from torch import nn |
|
|
| from common.utils import HiddenData, OutputData, InputData |
| from model.decoder import BaseDecoder |
| from model.decoder.interaction.gl_gin_interaction import LSTMEncoder |
|
|
|
|
| class IntentEncoder(nn.Module): |
| def __init__(self,input_dim, dropout_rate): |
| super().__init__() |
| self.dropout_rate = dropout_rate |
| self.__intent_lstm = LSTMEncoder( |
| input_dim, |
| input_dim, |
| dropout_rate |
| ) |
|
|
| def forward(self, g_hiddens, seq_lens): |
| intent_lstm_out = self.__intent_lstm(g_hiddens, seq_lens) |
| return F.dropout(intent_lstm_out, p=self.dropout_rate, training=self.training) |
|
|
|
|
| class GLGINDecoder(BaseDecoder): |
| def __init__(self, intent_classifier, slot_classifier, interaction=None, **config): |
| super().__init__(intent_classifier, slot_classifier, interaction) |
| self.config=config |
| self.intent_encoder = IntentEncoder(self.intent_classifier.config["input_dim"], self.config["dropout_rate"]) |
|
|
| def forward(self, hidden: HiddenData, forced_slot=None, forced_intent=None, differentiable=None): |
| seq_lens = hidden.inputs.attention_mask.sum(-1) |
| intent_lstm_out = self.intent_encoder(hidden.slot_hidden, seq_lens) |
| hidden.update_intent_hidden_state(intent_lstm_out) |
| pred_intent = self.intent_classifier(hidden) |
| intent_index = self.intent_classifier.decode(OutputData(pred_intent, None),hidden.inputs, |
| return_list=False, |
| return_sentence_level=True) |
| slot_hidden = self.interaction( |
| hidden, |
| pred_intent=pred_intent, |
| intent_index=intent_index, |
| ) |
| pred_slot = self.slot_classifier(slot_hidden) |
| num_intent = self.intent_classifier.config["intent_label_num"] |
| pred_slot = pred_slot.classifier_output[:, num_intent:] |
| return OutputData(pred_intent, F.log_softmax(pred_slot, dim=1)) |
