| from transformers.models.xlm_roberta import XLMRobertaPreTrainedModel, XLMRobertaModel |
| from transformers.modeling_outputs import TokenClassifierOutput |
| import torch |
| from torch import nn |
| from torch.nn import CrossEntropyLoss |
| from typing import Optional, Tuple, Union |
|
|
|
|
| class XLMRobertaForReferenceSegmentation(XLMRobertaPreTrainedModel): |
| _keys_to_ignore_on_load_unexpected = [r"pooler"] |
| _keys_to_ignore_on_load_missing = [r"position_ids"] |
|
|
| def __init__(self, config): |
| super().__init__(config) |
| self.num_labels_first = config.num_labels_first |
| self.num_labels_second = config.num_labels_second |
| self.alpha = config.alpha |
|
|
| self.roberta = XLMRobertaModel(config, add_pooling_layer=False) |
| classifier_dropout = ( |
| config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob |
| ) |
| self.dropout = nn.Dropout(classifier_dropout) |
| self.classifier_first = nn.Linear(config.hidden_size, self.num_labels_first) |
| self.classifier_second = nn.Linear(config.hidden_size, self.num_labels_second) |
|
|
| self.post_init() |
|
|
| def forward( |
| self, |
| input_ids: Optional[torch.LongTensor] = None, |
| attention_mask: Optional[torch.FloatTensor] = None, |
| token_type_ids: Optional[torch.LongTensor] = None, |
| position_ids: Optional[torch.LongTensor] = None, |
| head_mask: Optional[torch.FloatTensor] = None, |
| inputs_embeds: Optional[torch.FloatTensor] = None, |
| labels_first: Optional[torch.LongTensor] = None, |
| labels_second: Optional[torch.LongTensor] = None, |
| output_attentions: Optional[bool] = None, |
| output_hidden_states: Optional[bool] = None, |
| return_dict: Optional[bool] = None, |
| ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: |
| r""" |
| labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
| Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. |
| """ |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
| outputs = self.roberta( |
| input_ids, |
| attention_mask=attention_mask, |
| token_type_ids=token_type_ids, |
| position_ids=position_ids, |
| head_mask=head_mask, |
| inputs_embeds=inputs_embeds, |
| output_attentions=output_attentions, |
| output_hidden_states=output_hidden_states, |
| return_dict=return_dict, |
| ) |
|
|
| sequence_output = outputs[0] |
|
|
| sequence_output_first = self.dropout(sequence_output) |
| logits_first = self.classifier_first(sequence_output_first) |
|
|
| sequence_output_second = self.dropout(sequence_output) |
| logits_second = self.classifier_second(sequence_output_second) |
|
|
| loss = None |
| if labels_first is not None and labels_second is not None: |
| loss_fct_first = CrossEntropyLoss() |
| loss_fct_second = CrossEntropyLoss() |
| loss_first = loss_fct_first(logits_first.view(-1, self.num_labels_first), labels_first.view(-1)) |
| loss_second = loss_fct_second(logits_second.view(-1, self.num_labels_second), labels_second.view(-1)) |
| loss = loss_first + (self.alpha * loss_second) |
|
|
| return TokenClassifierOutput( |
| loss=loss, |
| logits=[logits_first, logits_second], |
| hidden_states=outputs.hidden_states, |
| attentions=outputs.attentions, |
| ) |
|
|
