| 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, | |
| ) | |