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| from models.med import BertConfig, BertModel, BertLMHeadModel | |
| from models.blip import create_vit, init_tokenizer, load_checkpoint | |
| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from transformers import BertTokenizer | |
| import numpy as np | |
| class BLIP_VQA(nn.Module): | |
| def __init__(self, | |
| med_config = 'configs/med_config.json', | |
| image_size = 480, | |
| vit = 'base', | |
| vit_grad_ckpt = False, | |
| vit_ckpt_layer = 0, | |
| ): | |
| """ | |
| Args: | |
| med_config (str): path for the mixture of encoder-decoder model's configuration file | |
| image_size (int): input image size | |
| vit (str): model size of vision transformer | |
| """ | |
| super().__init__() | |
| self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) | |
| self.tokenizer = init_tokenizer() | |
| encoder_config = BertConfig.from_json_file(med_config) | |
| encoder_config.encoder_width = vision_width | |
| self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) | |
| decoder_config = BertConfig.from_json_file(med_config) | |
| self.text_decoder = BertLMHeadModel(config=decoder_config) | |
| def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128): | |
| image_embeds = self.visual_encoder(image) | |
| image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) | |
| question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, | |
| return_tensors="pt").to(image.device) | |
| question.input_ids[:,0] = self.tokenizer.enc_token_id | |
| if train: | |
| ''' | |
| n: number of answers for each question | |
| weights: weight for each answer | |
| ''' | |
| answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) | |
| answer.input_ids[:,0] = self.tokenizer.bos_token_id | |
| answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100) | |
| question_output = self.text_encoder(question.input_ids, | |
| attention_mask = question.attention_mask, | |
| encoder_hidden_states = image_embeds, | |
| encoder_attention_mask = image_atts, | |
| return_dict = True) | |
| question_states = [] | |
| question_atts = [] | |
| for b, n in enumerate(n): | |
| question_states += [question_output.last_hidden_state[b]]*n | |
| question_atts += [question.attention_mask[b]]*n | |
| question_states = torch.stack(question_states,0) | |
| question_atts = torch.stack(question_atts,0) | |
| answer_output = self.text_decoder(answer.input_ids, | |
| attention_mask = answer.attention_mask, | |
| encoder_hidden_states = question_states, | |
| encoder_attention_mask = question_atts, | |
| labels = answer_targets, | |
| return_dict = True, | |
| reduction = 'none', | |
| ) | |
| loss = weights * answer_output.loss | |
| loss = loss.sum()/image.size(0) | |
| return loss | |
| else: | |
| question_output = self.text_encoder(question.input_ids, | |
| attention_mask = question.attention_mask, | |
| encoder_hidden_states = image_embeds, | |
| encoder_attention_mask = image_atts, | |
| return_dict = True) | |
| if inference=='generate': | |
| num_beams = 3 | |
| question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) | |
| question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) | |
| model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} | |
| bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) | |
| outputs = self.text_decoder.generate(input_ids=bos_ids, | |
| max_length=10, | |
| min_length=1, | |
| num_beams=num_beams, | |
| eos_token_id=self.tokenizer.sep_token_id, | |
| pad_token_id=self.tokenizer.pad_token_id, | |
| **model_kwargs) | |
| answers = [] | |
| for output in outputs: | |
| answer = self.tokenizer.decode(output, skip_special_tokens=True) | |
| answers.append(answer) | |
| return answers | |
| elif inference=='rank': | |
| max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, | |
| answer.input_ids, answer.attention_mask, k_test) | |
| return max_ids | |
| def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k): | |
| num_ques = question_states.size(0) | |
| start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token | |
| start_output = self.text_decoder(start_ids, | |
| encoder_hidden_states = question_states, | |
| encoder_attention_mask = question_atts, | |
| return_dict = True, | |
| reduction = 'none') | |
| logits = start_output.logits[:,0,:] # first token's logit | |
| # topk_probs: top-k probability | |
| # topk_ids: [num_question, k] | |
| answer_first_token = answer_ids[:,1] | |
| prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) | |
| topk_probs, topk_ids = prob_first_token.topk(k,dim=1) | |
| # answer input: [num_question*k, answer_len] | |
| input_ids = [] | |
| input_atts = [] | |
| for b, topk_id in enumerate(topk_ids): | |
| input_ids.append(answer_ids.index_select(dim=0, index=topk_id)) | |
| input_atts.append(answer_atts.index_select(dim=0, index=topk_id)) | |
| input_ids = torch.cat(input_ids,dim=0) | |
| input_atts = torch.cat(input_atts,dim=0) | |
| targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100) | |
| # repeat encoder's output for top-k answers | |
| question_states = tile(question_states, 0, k) | |
| question_atts = tile(question_atts, 0, k) | |
| output = self.text_decoder(input_ids, | |
| attention_mask = input_atts, | |
| encoder_hidden_states = question_states, | |
| encoder_attention_mask = question_atts, | |
| labels = targets_ids, | |
| return_dict = True, | |
| reduction = 'none') | |
| log_probs_sum = -output.loss | |
| log_probs_sum = log_probs_sum.view(num_ques,k) | |
| max_topk_ids = log_probs_sum.argmax(dim=1) | |
| max_ids = topk_ids[max_topk_ids>=0,max_topk_ids] | |
| return max_ids | |
| def blip_vqa(pretrained='',**kwargs): | |
| model = BLIP_VQA(**kwargs) | |
| if pretrained: | |
| model,msg = load_checkpoint(model,pretrained) | |
| # assert(len(msg.missing_keys)==0) | |
| return model | |
| def tile(x, dim, n_tile): | |
| init_dim = x.size(dim) | |
| repeat_idx = [1] * x.dim() | |
| repeat_idx[dim] = n_tile | |
| x = x.repeat(*(repeat_idx)) | |
| order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])) | |
| return torch.index_select(x, dim, order_index.to(x.device)) | |