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))