| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from torch.nn import init |
| import copy |
| from config4LXMT5_DDP import args |
| import collections |
| from transformers import LxmertConfig, LxmertTokenizer, LxmertModel,BertTokenizer |
| from transformers import T5Tokenizer, T5Model, T5Config, T5ForConditionalGeneration |
| from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions |
| T5tokenizer = T5Tokenizer.from_pretrained("../model/t5-large") |
| LXMtokenizer = BertTokenizer.from_pretrained('../model/bert-base-uncased/vocab.txt') |
| T5config = T5Config.from_pretrained('../model/t5-large') |
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| device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
| LXM_source_masks=None,token_type_ids=None, visual_features=None, spatial_features=None,T5_target_ids=None,T5_target_masks=None): |
| attention_mask=LXM_source_masks, token_type_ids=token_type_ids, visual_feats=visual_features, visual_pos=spatial_features) |
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| class LXMT52T5(nn.Module): |
| def __init__(self): |
| super(LXMT52T5, self).__init__() |
| self.T5model = T5ForConditionalGeneration.from_pretrained("../model/t5-large").to(device) |
| self.LXMmodel = LxmertModel.from_pretrained('../model/lxmert-base-uncased').to(device) |
| self.mapping = torch.nn.Sequential( |
| torch.nn.Linear(768, 1024), |
| torch.nn.ReLU(inplace=True), |
| torch.nn.Linear(1024, 1024) |
| ) |
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| def LXMT5end2T5dec(self, train=None, LXM_source_ids=None, LXM_source_masks=None,T5_source_ids=None, T5_source_masks=None,token_type_ids=None, visual_features=None, spatial_features=None,T5_target_ids=None,T5_target_masks=None): |
| |
| if 1: |
| LXM_encoder_output_seq = self.LXMmodel(input_ids=LXM_source_ids, attention_mask=LXM_source_masks, token_type_ids=token_type_ids, visual_feats=visual_features, visual_pos=spatial_features) |
| LXM_lang_enc_out = LXM_encoder_output_seq.language_output |
| LXM_visual_enc_out = LXM_encoder_output_seq.vision_output |
| |
| LXM_VL_encoder_output_seq = torch.cat((LXM_lang_enc_out, LXM_visual_enc_out),1) |
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| if 1: |
| final_encoder_output_seq_list = [] |
| final_T5_encoder_output_seq_list = [] |
| |
| for ind in range(args.num_wiki): |
| T5_encoder_output_seq = self.T5model.encoder(input_ids=T5_source_ids[:,ind,:], attention_mask=T5_source_masks[:,ind,:]) |
| |
| |
| tmp_encoder_output_seq = torch.cat((final_LXM_encoder_output_seq, T5_encoder_output_seq["last_hidden_state"]),1) |
| final_encoder_output_seq_list.append(tmp_encoder_output_seq) |
| final_encoder_output_seq = torch.cat(final_encoder_output_seq_list,1) |
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| final_encoder_output_seq = final_LXM_encoder_output_seq |
| |
| final_encoder_output_seq = torch.cat(final_T5_encoder_output_seq_list,1) |
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| my_order_dict=T5_encoder_output_seq |
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| my_order_dict.last_hidden_state=final_encoder_output_seq |
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| if train: |
| if args.allAns: |
| outputs = self.T5model(encoder_outputs=my_order_dict, labels=T5_target_ids, decoder_attention_mask=T5_target_masks) |
| else: |
| outputs = self.T5model(encoder_outputs=my_order_dict, labels=T5_target_ids, decoder_attention_mask=T5_target_masks) |
| return outputs |
| else: |
| if torch.cuda.device_count() > 1: |
| pred = self.T5model.generate(encoder_outputs=my_order_dict) |
| else: |
| pred = self.T5model.generate(encoder_outputs=my_order_dict) |
| return pred |
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| def forward(self, train=None, LXM_source_ids=None, LXM_source_masks=None,T5_source_ids=None, T5_source_masks=None,token_type_ids=None, visual_features=None, spatial_features=None,T5_target_ids=None,T5_target_masks=None): |
| return self.LXMT5end2T5dec(train, LXM_source_ids, LXM_source_masks, T5_source_ids, T5_source_masks, token_type_ids, visual_features, spatial_features, T5_target_ids, T5_target_masks) |
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