remove unsued import

bert_for_sequence_classification.py

@@ -1,14 +1,9 @@
 import torch
 import transformers
 from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from typing import List, Optional, Tuple, Union
-
-from transformers import BertTokenizer
-from transformers import models, DataCollatorWithPadding, AutoTokenizer
+from torch.nn import CrossEntropyLoss
+from typing import Optional, Tuple, Union
 from transformers.modeling_outputs import SequenceClassifierOutput
-
-from transformers.models.bert.configuration_bert import BertConfig
 from transformers.models.bert.modeling_bert import (
     BertPreTrainedModel,
     BERT_INPUTS_DOCSTRING,
@@ -35,111 +30,110 @@ from transformers.file_utils import (
     BERT_START_DOCSTRING,
 )
 class BertForSequenceClassification(BertPreTrainedModel):
-  def __init__(self, config, **kwargs):
-    super().__init__(transformers.PretrainedConfig())
-    #task_labels_map={"binary_classification": 2, "label_classification": 5}
-    self.tasks = kwargs.get("tasks_map", {})
-    self.config = config
+    def __init__(self, config, **kwargs):
+        super().__init__(transformers.PretrainedConfig())
+        #task_labels_map={"binary_classification": 2, "label_classification": 5}
+        self.tasks = kwargs.get("tasks_map", {})
+        self.config = config
 
-    self.bert = BertModel(config)
-    classifier_dropout = (
-        config.classifier_dropout
-        if config.classifier_dropout is not None
-        else config.hidden_dropout_prob
-    )
-    self.dropout = nn.Dropout(classifier_dropout)
-    ## add task specific output heads
-    self.classifier1 = nn.Linear(
-        config.hidden_size, self.tasks[0].num_labels
-    )
-    self.classifier2 = nn.Linear(
-        config.hidden_size, self.tasks[1].num_labels
-    )
+        self.bert = BertModel(config)
+        classifier_dropout = (
+            config.classifier_dropout
+            if config.classifier_dropout is not None
+            else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        ## add task specific output heads
+        self.classifier1 = nn.Linear(
+            config.hidden_size, self.tasks[0].num_labels
+        )
+        self.classifier2 = nn.Linear(
+            config.hidden_size, self.tasks[1].num_labels
+        )
 
-    self.init_weights()
+        self.init_weights()
 
-  @add_start_docstrings_to_model_forward(
-  BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
-  )
-  @add_code_sample_docstrings(
-      processor_class=_TOKENIZER_FOR_DOC,
-      checkpoint=_CHECKPOINT_FOR_DOC,
-      output_type=SequenceClassifierOutput,
-      config_class=_CONFIG_FOR_DOC,
-      expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
-      expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
-  )
-  def forward(
-    self,
-    input_ids: Optional[torch.Tensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    token_type_ids: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.Tensor] = None,
-    head_mask: Optional[torch.Tensor] = None,
-    inputs_embeds: Optional[torch.Tensor] = None,
-    labels: Optional[torch.Tensor] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    task_ids=None,
-) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
-    r"""
-    labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-        Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
-        config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
-        If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-    """
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
+    @add_start_docstrings_to_model_forward(
+    BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
     )
-
-    outputs = self.bert(
-        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,
+    @add_code_sample_docstrings(
+        processor_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+        expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
+        expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
     )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        task_ids=None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
 
-    pooled_output = outputs[1]
+        outputs = self.bert(
+            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,
+        )
 
-    pooled_output = self.dropout(pooled_output)
-  
-    unique_task_ids_list = torch.unique(task_ids).tolist()
-    loss_list = []
-    logits = None
-    for unique_task_id in unique_task_ids_list:
+        pooled_output = outputs[1]
 
-      loss = None
-      task_id_filter = task_ids == unique_task_id 
+        pooled_output = self.dropout(pooled_output)
+    
+        unique_task_ids_list = torch.unique(task_ids).tolist()
+        loss_list = []
+        logits = None
+        for unique_task_id in unique_task_ids_list:
+            loss = None
+            task_id_filter = task_ids == unique_task_id 
 
-      if unique_task_id == 0:
-        logits = self.classifier1(pooled_output[task_id_filter])
-      elif unique_task_id == 1:
-        logits = self.classifier2(pooled_output[task_id_filter])
+            if unique_task_id == 0:
+                logits = self.classifier1(pooled_output[task_id_filter])
+            elif unique_task_id == 1:
+                logits = self.classifier2(pooled_output[task_id_filter])
 
-      
-      if labels is not None: 
-        loss_fct = CrossEntropyLoss()
-        loss = loss_fct(logits.view(-1, self.tasks[unique_task_id].num_labels), labels[task_id_filter].view(-1))
-        loss_list.append(loss)
-    
-    # logits are only used for eval. and in case of eval the batch is not multi task
-    # For training only the loss is used
+            
+            if labels is not None: 
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.tasks[unique_task_id].num_labels), labels[task_id_filter].view(-1))
+                loss_list.append(loss)
+        
+        # logits are only used for eval. and in case of eval the batch is not multi task
+        # For training only the loss is used
 
-    if loss_list:
-      loss = torch.stack(loss_list).mean()
-    if not return_dict:
-      output = (logits,) + outputs[2:]
-      return ((loss,) + output) if loss is not None else output
-    
-    return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
+        if loss_list:
+            loss = torch.stack(loss_list).mean()
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+        
+        return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )