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| import torch | |
| import torch.nn as nn | |
| from transformers import * | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| # pretrained model name: (model class, model tokenizer, output dimension, token style) | |
| MODELS = { | |
| 'prajjwal1/bert-mini': (BertModel, BertTokenizer), | |
| } | |
| class Text_Encoder(nn.Module): | |
| def __init__(self, device): | |
| super(Text_Encoder, self).__init__() | |
| self.base_model = 'prajjwal1/bert-mini' | |
| self.dropout = 0.1 | |
| self.tokenizer = MODELS[self.base_model][1].from_pretrained(self.base_model) | |
| self.bert_layer = MODELS[self.base_model][0].from_pretrained(self.base_model, | |
| add_pooling_layer=False, | |
| hidden_dropout_prob=self.dropout, | |
| attention_probs_dropout_prob=self.dropout, | |
| output_hidden_states=True) | |
| self.linear_layer = nn.Sequential(nn.Linear(256, 256), nn.ReLU(inplace=True)) | |
| self.device = device | |
| def tokenize(self, caption): | |
| # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| tokenized = self.tokenizer(caption, add_special_tokens=False, padding=True, return_tensors='pt') | |
| input_ids = tokenized['input_ids'] | |
| attns_mask = tokenized['attention_mask'] | |
| input_ids = input_ids.to(self.device) | |
| attns_mask = attns_mask.to(self.device) | |
| return input_ids, attns_mask | |
| def forward(self, input_ids, attns_mask): | |
| # input_ids, attns_mask = self.tokenize(caption) | |
| output = self.bert_layer(input_ids=input_ids, attention_mask=attns_mask)[0] | |
| cls_embed = output[:, 0, :] | |
| text_embed = self.linear_layer(cls_embed) | |
| return text_embed, output # text_embed: (batch, hidden_size) |