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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple, Union
import torch
from mmengine.model import BaseModel
from mmpretrain.registry import MODELS, TOKENIZER
from mmpretrain.structures import DataSample
@MODELS.register_module()
class BlipVQA(BaseModel):
"""BLIP VQA.
Args:
tokenizer: (dict): The config for tokenizer.
vision_backbone (dict): Encoder for extracting image features.
multimodal_backbone (dict): Backbone for extracting
multi-modal features. We apply this part as VQA fusion module.
head (dict): The head module to calculate
loss from processed features.
data_preprocessor (Optional[dict]): The config for preprocessing input
data. If None or no specified type, it will use
`MutimodalDataPreprocessor` as type.
See :class:`MutimodalDataPreprocessor` for more details.
Defaults to None.
init_cfg (Optional[dict]): the config to control the initialization.
Defaults to None.
"""
def __init__(self,
tokenizer: dict,
vision_backbone: dict,
multimodal_backbone: dict,
head: dict,
data_preprocessor: Optional[dict] = None,
init_cfg: Optional[dict] = None):
if data_preprocessor is None:
data_preprocessor = {}
data_preprocessor.setdefault('type', 'MultiModalDataPreprocessor')
data_preprocessor = MODELS.build(data_preprocessor)
super(BlipVQA, self).__init__(
init_cfg=init_cfg, data_preprocessor=data_preprocessor)
self.tokenizer = TOKENIZER.build(tokenizer)
self.vision_backbone = MODELS.build(vision_backbone)
self.multimodal_backbone = MODELS.build(multimodal_backbone)
self.vqa_head = MODELS.build(head)
@property
def device(self):
return next(self.parameters()).device
def forward(
self,
images: torch.Tensor,
data_samples: Optional[List[DataSample]] = None,
mode: str = 'loss',
):
"""The unified entry for a forward process in both training and test.
- "loss": For training. Forward and return a dict of losses according
to the given inputs and data samples. Note that this method doesn't
handle neither back propagation nor optimizer updating, which are
done in the :meth:`train_step`.
- "predict": For testing. Forward and return a list of data_sample that
contains pred_answer for each question.
Args:
images (Tensor): A batch of images. The shape of it should be
(B, C, H, W) for images and (B, T, C, H, W) for videos.
data_samples (List[DataSample], optional): The annotation data of
every samples. Required when ``mode="loss"``. Defaults to None.
mode (str): Return what kind of value. Defaults to 'loss'.
Returns:
The return type depends on ``mode``.
- If ``mode="loss"``, return a dict of tensor.
- If ``mode="predict"``, return a list of `DataSample`
"""
if mode == 'loss':
return self.loss(images, data_samples)
elif mode == 'predict':
return self.predict(images, data_samples)
else:
raise RuntimeError(f'Invalid mode "{mode}".')
def extract_feat(self, images: torch.Tensor) -> torch.Tensor:
"""Extract features from the input tensor with shape (N, C, ..).
Args:
images (Tensor): A batch of images. The shape of it should be
(B, C, H, W) for images and (B, T, C, H, W) for videos.
Returns:
visual_embeds (Tensor): The output features.
"""
# extract visual feature
if images.ndim == 4:
visual_embeds = self.vision_backbone(images)[0]
elif images.ndim == 5:
# [batch, T, C, H, W] -> [batch * T, C, H, W]
bs = images.size(0)
images = images.reshape(-1, *images.shape[2:])
visual_embeds = self.vision_backbone(images)[0]
# [batch * num_segs, L, dim] -> [batch, num_segs * L, dim]
visual_embeds = visual_embeds.reshape(bs, -1,
*visual_embeds.shape[2:])
else:
raise ValueError(
f'Images with {images.ndim} dims is not supported.')
return visual_embeds
def loss(
self,
images: torch.Tensor,
data_samples: Optional[List[DataSample]] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor]]:
"""generate train_loss from the input tensor and data_samples.
Args:
images (Tensor): A batch of images. The shape of it should be
(B, C, H, W) for images and (B, T, C, H, W) for videos.
data_samples (List[DataSample], optional): The annotation
data of every samples.
Returns:
Dict[torch.Tensor]: The losses features.
"""
visual_embeds = self.extract_feat(images)
image_atts = torch.ones(
visual_embeds.size()[:-1], dtype=torch.long).to(self.device)
questions = []
for sample in data_samples:
questions.append(sample.get('question'))
questions = self.tokenizer(
questions, padding='longest', return_tensors='pt').to(self.device)
questions.input_ids[:, 0] = \
self.tokenizer.additional_special_tokens_ids[0]
# multimodal fusion
multimodal_embeds = self.multimodal_backbone(
questions.input_ids,
attention_mask=questions.attention_mask,
encoder_hidden_states=visual_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
# put answer from data_samples into tensor form
answer_raw_text = []
for sample in data_samples:
answer_raw_text.extend(sample.gt_answer)
answer = self.tokenizer(
answer_raw_text, padding='longest',
return_tensors='pt').to(self.device)
answer_targets = answer.input_ids.masked_fill(
answer.input_ids == self.tokenizer.pad_token_id, -100)
for sample in data_samples:
# follow BLIP setting, set answer_weight to 0.2 for VG dataset.
if not hasattr(sample, 'gt_answer_weight'):
sample.gt_answer_weight = torch.tensor([0.2])
else:
sample.gt_answer_weight = torch.tensor(sample.gt_answer_weight)
answer_weight = torch.cat(
[sample.gt_answer_weight for sample in data_samples],
dim=0).to(self.device)
answer_count = torch.tensor(
[len(sample.gt_answer) for sample in data_samples]).to(self.device)
question_states, question_atts = [], []
for b, n in enumerate(answer_count):
question_states += [multimodal_embeds.last_hidden_state[b]] * n
question_atts += [questions.attention_mask[b]] * n
question_states = torch.stack(question_states, dim=0).to(self.device)
question_atts = torch.stack(question_atts, dim=0).to(self.device)
head_feats = dict(
answer_input_ids=answer.input_ids,
answer_attention_mask=answer.attention_mask,
answer_weight=answer_weight,
answer_targets=answer_targets,
question_states=question_states,
question_atts=question_atts,
batch_size=len(data_samples),
)
losses = self.vqa_head.loss(head_feats)
return losses
def predict(
self,
images: torch.Tensor,
data_samples: Optional[List[DataSample]] = None,
):
"""update data_samples that contain pred_answer for each question.
Args:
images (Tensor): A batch of images. The shape of it should be
(B, C, H, W) for images and (B, T, C, H, W) for videos.
data_samples (List[DataSample], optional): The annotation
data of every samples.
Returns:
Dict[torch.Tensor]: The losses features.
"""
visual_embeds = self.extract_feat(images)
image_atts = torch.ones(
visual_embeds.size()[:-1], dtype=torch.long).to(self.device)
questions = []
for sample in data_samples:
questions.append(sample.get('question'))
questions = self.tokenizer(
questions, padding='longest', return_tensors='pt').to(self.device)
questions.input_ids[:, 0] = \
self.tokenizer.additional_special_tokens_ids[0]
# multimodal fusion
multimodal_embeds = self.multimodal_backbone(
questions.input_ids,
attention_mask=questions.attention_mask,
encoder_hidden_states=visual_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
if self.vqa_head.inference_method == 'rank':
answer_candidates = self.tokenizer(
self.vqa_head.answer_list,
padding='longest',
return_tensors='pt').to(self.device)
answer_candidates.input_ids[:, 0] = self.tokenizer.bos_token_id
elif self.vqa_head.inference_method == 'generate':
answer_candidates = None
head_feats = dict(
multimodal_embeds=multimodal_embeds.last_hidden_state,
question_atts=questions.attention_mask,
answer_candidates=answer_candidates,
bos_token_id=self.tokenizer.bos_token_id,
sep_token_id=self.tokenizer.sep_token_id,
pad_token_id=self.tokenizer.pad_token_id,
)
if self.vqa_head.inference_method == 'rank':
answers = self.vqa_head.predict(head_feats)
for answer, data_sample in zip(answers, data_samples):
data_sample.pred_answer = answer
elif self.vqa_head.inference_method == 'generate':
outputs = self.vqa_head.predict(head_feats)
for output, data_sample in zip(outputs, data_samples):
data_sample.pred_answer = self.tokenizer.decode(
output, skip_special_tokens=True)
return data_samples
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