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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import mmengine
import torch
import torch.nn.functional as F
from einops import rearrange
from mmaction.registry import MODELS
from .vindlu import VindLUBase
@MODELS.register_module()
class VindLUVQA(VindLUBase):
"""VindLU VQA.
Args:
text_decoder (dict): Backbone for extracting
multi-modal features. We apply this part as VQA fusion module.
answer_list_path (str, optional): Path to `answer_list.json`.
max_question_len (int): Max text length of question text.
Defaults to 25.
max_answer_len (int): Max text length of answer text. Defaults to 5.
num_ans_candidates (int): Number of answer candidates, used to filter
out answers with low probability. Defaults to 128.
**kwargs: Other keyword arguments accepted by the VindLUBase.
"""
def __init__(self,
text_decoder: dict,
answer_list_path: Optional[str] = None,
max_question_len: int = 25,
max_answer_len: int = 5,
num_ans_candidates: int = 128,
**kwargs):
super().__init__(**kwargs)
self.max_question_len = max_question_len
self.max_answer_len = max_answer_len
self.num_ans_candidates = num_ans_candidates
self.answer_list_path = answer_list_path
self.text_decoder_cfg = text_decoder
# for inference only
if answer_list_path:
self.answer_list = mmengine.load(answer_list_path)
# delete extra/unnecessary modules inherited from VindLUBase
extra_attributes = ['vision_proj', 'text_proj', 'temp', 'itm_head']
for attr in extra_attributes:
delattr(self, attr)
self.text_decoder_cfg.gradient_checkpointing = \
self.gradient_checkpointing
self.text_decoder = MODELS.build(self.text_decoder_cfg)
def forward_encoder(self, inputs, data_samples):
# forward vision encoder
image_embeds, _ = self.encode_vision(inputs)
image_embeds = rearrange(image_embeds, 'b t l c -> b (t l) c')
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(inputs.device)
# forward text encoder
questions = [sample.question for sample in data_samples]
questions = self.tokenizer(
questions,
padding='max_length',
truncation=True,
max_length=self.max_question_len,
return_tensors='pt').to(inputs.device)
question_output = self.text_encoder(
questions.input_ids,
attention_mask=questions.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True)
return questions, question_output
def loss(self, inputs, data_samples):
"""Calculate losses from a batch of inputs and data samples.
Args:
inputs (dict): A batch of inputs. The input tensor with of
at least one modality. For image, the value is a tensor
of shape (N, C, ...) in general.
For text, the value is a dict of tokenized text inputs.
data_samples (Optional[List[DataSample]]):
The annotation data of every samples. Defaults to None.
Returns:
Dict[str, torch.tensor]: a dictionary of loss components of
"""
questions, question_output = self.forward_encoder(inputs, data_samples)
weights = torch.cat(
[torch.tensor(sample.gt_answer_weight) for sample in data_samples],
dim=0).to(inputs.device)
raw_answers = []
for sample in data_samples:
raw_answers.extend(sample.gt_answer)
answer_count = torch.tensor([
len(sample.gt_answer) for sample in data_samples
]).to(inputs.device)
answers = [a + ' ' + '[SEP]' for a in raw_answers]
answers = self.tokenizer(
answers,
padding='max_length',
truncation=True,
max_length=self.max_answer_len,
return_tensors='pt').to(inputs.device)
answer_targets = answers.input_ids.masked_fill(
answers.input_ids == self.tokenizer.pad_token_id, -100)
question_states = []
question_atts = []
for b, n in enumerate(answer_count):
question_states += [question_output.last_hidden_state[b]] * n
question_atts += [questions.attention_mask[b]] * n
question_states = torch.stack(question_states, 0).to(inputs.device)
question_atts = torch.stack(question_atts, 0).to(inputs.device)
answer_output = self.text_decoder(
answers.input_ids,
attention_mask=answers.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() / inputs.size(0)
return dict(loss=loss)
def predict(self, inputs, data_samples, **kwargs):
questions, question_output = self.forward_encoder(inputs, data_samples)
raw_answers = self.answer_list
answers = [a + ' ' + '[SEP]' for a in raw_answers]
answers = self.tokenizer(
answers,
padding='max_length',
truncation=True,
max_length=self.max_answer_len,
return_tensors='pt',
).to(inputs.device)
topk_ids, topk_probs = self.rank_answer(
question_output.last_hidden_state, questions.attention_mask,
answers.input_ids, answers.attention_mask, self.num_ans_candidates)
out_data_samples = []
for data_sample, topk_id, topk_prob in zip(data_samples, topk_ids,
topk_probs):
_, pred = topk_prob.max(dim=0)
data_sample.pred_answer = raw_answers[topk_id[pred]]
out_data_samples.append(data_sample)
return out_data_samples
def rank_answer(self, question_states, question_atts, answer_ids,
answer_atts, k):
"""
question_states: (bsz, Lq, d)
answer_ids: answer input id after tokenization, (#answers, La)
"""
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)
question_states = question_states.repeat_interleave(k, dim=0)
question_atts = question_atts.repeat_interleave(k, dim=0)
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',
)
answer_loss = output.loss
answer_loss = answer_loss.view(input_ids.size(0), -1)
# topk_prob: first token probability
topk_probs = topk_probs.view(-1, 1)
log_probs = torch.cat([topk_probs.log(), -answer_loss], dim=1)
# re-calculate log probabilities for the answer sequences
# using chain rule
log_probs_sum = log_probs.sum(1)
log_probs_sum = log_probs_sum.view(num_ques, k)
topk_probs = F.softmax(log_probs_sum, dim=-1)
# get top-k after re-ranking
topk_probs, rerank_id = topk_probs.topk(k, dim=1)
topk_ids = torch.gather(topk_ids, 1, rerank_id)
return topk_ids, topk_probs
def preprocess_state_dict(self, state_dict):
"""Preprocess pretrained checkpoint for text_encoder and
text_decoder."""
for key in list(state_dict.keys()):
if 'bert' in key:
encoder_key = key.replace('bert.', '')
state_dict[encoder_key] = state_dict[key]
# init text decoder as multimodal encoder
# (last 6 layers of model.text_encoder)
# only for generation tasks like VQA
if self.text_decoder_cfg and 'text_encoder' in key:
if 'layer' in key:
encoder_keys = key.split('.')
layer_num = int(encoder_keys[4])
if layer_num < self.text_encoder_cfg.fusion_layer:
del state_dict[key]
continue
else:
decoder_layer_num = layer_num - 9
encoder_keys[4] = str(decoder_layer_num)
encoder_key = '.'.join(encoder_keys)
else:
encoder_key = key
decoder_key = encoder_key.replace('text_encoder',
'text_decoder')
state_dict[decoder_key] = state_dict[key]
del state_dict[key]
return state_dict
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