mmpretrain.models.multimodal.blip2.blip2_opt_vqa 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional

import torch

from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from .blip2_caption import Blip2Caption

[文档]@MODELS.register_module() class Blip2VQA(Blip2Caption): """BLIP2 VQA. Module for BLIP2 VQA task. For more details about the initialization params, please refer to :class:`Blip2Caption`. """
[文档] def predict(self, images: torch.Tensor, data_samples: Optional[list] = None, **kwargs) -> List[DataSample]: """Predict captions from a batch of inputs. Args: images (torch.Tensor): The input tensor with shape (N, C, ...) in general. data_samples (List[DataSample], optional): The annotation data of every samples. Defaults to None. **kwargs: Other keyword arguments accepted by the ``predict`` method of :attr:`head`. Returns: List[DataSample]: Return list of data samples. """ questions = [d.question for d in data_samples] # extract image features from image_embeds = self.ln_vision_backbone(self.vision_backbone(images)[0]) image_atts = torch.ones( image_embeds.size()[:-1], dtype=torch.long, ).to(images.device) # distill image features to query tokens query_tokens = self.query_tokens.expand(image_embeds.size(0), -1, -1) query_outputs = self.multimodal_backbone.bert( query_embeds=query_tokens, encoder_hidden_states=image_embeds, encoder_attention_mask=image_atts, return_dict=True, ) inputs_opt = self.vision_neck([query_outputs.last_hidden_state]) attns_opt = torch.ones( inputs_opt.size()[:-1], dtype=torch.long).to(images.device) prompt = [self.prompt.format(q) for q in questions] # use left padding self.tokenizer.padding_side = 'left' opt_tokens = self.tokenizer( prompt, return_tensors='pt', padding='longest').to(images.device) input_ids = opt_tokens.input_ids attention_mask =[attns_opt, opt_tokens.attention_mask], dim=1) inputs_embeds = self.text_backbone.model.decoder.embed_tokens( input_ids) inputs_embeds =[inputs_opt, inputs_embeds], dim=1) outputs = self.text_backbone.generate( inputs_embeds=inputs_embeds, attention_mask=attention_mask, do_sample=False, num_beams=5, max_new_tokens=self.max_txt_len, min_length=1, eos_token_id=self.eos_token_id, length_penalty=-1.0, ) output_text = self.tokenizer.batch_decode( outputs, skip_special_tokens=True) output_text = [text.strip() for text in output_text] out_data_samples = [] for data_sample, decode_token in zip(data_samples, output_text): data_sample.pred_answer = decode_token out_data_samples.append(data_sample) return out_data_samples
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