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import torch
from transformers import AutoModelForTokenClassification, AutoModelForSequenceClassification, AdamW, get_linear_schedule_with_warmup
from transformers import BertForTokenClassification, BertForSequenceClassification,BertPreTrainedModel, BertModel
import torch.nn as nn
import torch.nn.functional as F
class BertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class Model_Rational_Label(BertPreTrainedModel):
def __init__(self,config):
super().__init__(config)
self.num_labels=2
self.impact_factor=0.8
self.bert = BertModel(config,add_pooling_layer=False)
self.bert_pooler=BertPooler(config)
self.token_dropout = nn.Dropout(0.1)
self.token_classifier = nn.Linear(config.hidden_size, 2)
self.dropout = nn.Dropout(0.1)
self.classifier = nn.Linear(config.hidden_size, self.num_labels)
self.init_weights()
# self.embeddings = AutoModelForTokenClassification.from_pretrained(params['model_path'], cache_dir=params['cache_path'])
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, attn=None, labels=None):
outputs = self.bert(input_ids, attention_mask)
# out = outputs.last_hidden_state
out=outputs[0]
logits = self.token_classifier(self.token_dropout(out))
# mean_pooling = torch.mean(out, 1)
# max_pooling, _ = torch.max(out, 1)
# embed = torch.cat((mean_pooling, max_pooling), 1)
embed=self.bert_pooler(outputs[0])
y_pred = self.classifier(self.dropout(embed))
loss_token = None
loss_label = None
loss_total = None
if attn is not None:
loss_fct = nn.CrossEntropyLoss()
# Only keep active parts of the loss
if mask is not None:
active_loss = mask.view(-1) == 1
active_logits = logits.view(-1, 2)
active_labels = torch.where(
active_loss, attn.view(-1), torch.tensor(loss_fct.ignore_index).type_as(attn)
)
loss_token = loss_fct(active_logits, active_labels)
else:
loss_token = loss_fct(logits.view(-1, 2), attn.view(-1))
loss_total=self.impact_factor*loss_token
if labels is not None:
loss_funct = nn.CrossEntropyLoss()
loss_logits = loss_funct(y_pred.view(-1, self.num_labels), labels.view(-1))
loss_label= loss_logits
if(loss_total is not None):
loss_total+=loss_label
else:
loss_total=loss_label
if(loss_total is not None):
return y_pred, logits, loss_total
else:
return y_pred, logits |