93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# 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
|
|
|
|
|
|
@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`.
|
|
"""
|
|
|
|
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 = torch.cat([attns_opt, opt_tokens.attention_mask],
|
|
dim=1)
|
|
|
|
inputs_embeds = self.text_backbone.model.decoder.embed_tokens(
|
|
input_ids)
|
|
inputs_embeds = torch.cat([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
|