Upload foodybert.py

foodybert.py

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+import os
+from sklearn.metrics import classification_report
+import torch.nn as nn
+import transformers
+from transformers import BertModel, BertTokenizer, BertForSequenceClassification
+import numpy as np
+from datasets import load_dataset, load_metric
+
+import math
+import warnings
+from dataclasses import dataclass
+import torch
+import torch.utils.checkpoint
+from packaging import version
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from typing import List, Optional, Tuple, Union
+import torch
+
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    NextSentencePredictorOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+
+
+class FoodyBertForSequenceClassification(BertForSequenceClassification):
+    def __init__(self, config):
+        super().__init__(config)
+        
+        self.num_labels = config.num_labels
+        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.pre_classifier = torch.nn.Linear(4*config.hidden_size, 4*config.hidden_size)
+        self.tanh = nn.Tanh()
+        #self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(4*config.hidden_size, config.num_labels)
+
+        self.post_init()
+    def post_init(self):
+        pass
+    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,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `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
+
+        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,
+        )
+        
+        # outputs is a tuple contains(last_hidden_state, pooler_output,hidden_states...+3more)
+        # [0] last_hidden_state -> tensor [batch, #tokens, 768]
+        # [1] pooler_output -> tensor [1, 768]
+        # [2] hidden_states -> tuple with 13 tensors of size [batch, #tokens,768]
+        
+        # use only CLS
+        #pooled_output = outputs[1]
+        
+        #average actoss tokens at the last layer
+        #last_state = outputs[0]
+        #pooled_output = torch.mean(last_state,1)
+        
+        # use hidden_states and concatenate layers -> change classifier dimensions!
+        hidden_states = outputs[2]
+        
+        #concatenate 4 layers and average tokens
+        pooled_output = torch.cat(tuple([hidden_states[i] for i in [-4,-3,-2,-1]]), dim = -1)
+        pooled_output = torch.mean(pooled_output,1)
+        
+        #concatenate 4 layers and use CLS
+        #pooled_output = torch.cat(tuple([hidden_states[i] for i in [-4,-3,-2,-1]]), dim = -1)
+        #pooled_output = pooled_output[:, 0, :]
+        
+        pooled_output = self.pre_classifier(pooled_output)
+        pooled_output = self.tanh(pooled_output)
+        #pooled_output = self.relu(pooled_output)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        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,
+        )