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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional
import torch
from mmengine.model import BaseModel
from torch import nn
from mmpretrain.registry import MODELS, TOKENIZER
from mmpretrain.structures import DataSample
@MODELS.register_module()
class Blip2Caption(BaseModel):
"""BLIP2 Caption.
Module for BLIP2 Caption task.
Args:
vision_backbone (dict): The config dict for vision backbone.
text_backbone (dict): The config dict for text backbone.
multimodal_backbone (dict): The config dict for multimodal backbone.
vision_neck (dict): The config dict for vision neck.
tokenizer: (Optional[dict]): The config for tokenizer.
Defaults to None.
prompt (str): Prompt used for training and eval.
Defaults to ''.
max_txt_len (int): Max text length of input text.
num_captions (int): Number of captions to be generated for each image.
data_preprocessor (Optional[dict]): The config for preprocessing input
data. If None or no specified type, it will use
"MultiModalDataPreprocessor" as type.
See :class:`MultiModalDataPreprocessor` for more details.
Defaults to None.
init_cfg (Optional[dict]): the config to control the initialization.
Defaults to None.
"""
_no_split_modules = ['BEiTViT', 'OPTDecoderLayer', 'BertLayer']
def __init__(self,
vision_backbone: dict,
text_backbone: dict,
multimodal_backbone: dict,
vision_neck: dict,
tokenizer: Optional[dict] = None,
prompt: str = '',
max_txt_len: int = 20,
num_captions: int = 1,
data_preprocessor: Optional[dict] = None,
init_cfg: Optional[dict] = None) -> None:
if data_preprocessor is None:
data_preprocessor = {}
if isinstance(data_preprocessor, dict):
data_preprocessor.setdefault('type', 'MultiModalDataPreprocessor')
data_preprocessor = MODELS.build(data_preprocessor)
super().__init__(
init_cfg=init_cfg, data_preprocessor=data_preprocessor)
self.tokenizer = TOKENIZER.build(tokenizer)
self.eos_token_id = self.tokenizer(
'\n', add_special_tokens=False).input_ids[0]
self.vision_backbone = MODELS.build(vision_backbone)
self.ln_vision_backbone = nn.LayerNorm(self.vision_backbone.embed_dims)
self.vision_neck = MODELS.build(vision_neck)
self.text_backbone = MODELS.build(text_backbone)
self.multimodal_backbone = MODELS.build(multimodal_backbone)
self.multimodal_backbone.cls = None
self.multimodal_backbone.bert.embeddings.word_embeddings = None
self.multimodal_backbone.bert.embeddings.position_embeddings = None
for layer in self.multimodal_backbone.bert.encoder.layer:
layer.output = None
layer.intermediate = None
self.prompt = prompt
self.max_txt_len = max_txt_len
self.num_captions = num_captions
prompt_tokens = self.tokenizer(prompt, return_tensors='pt')
self.prompt_length = prompt_tokens.attention_mask.sum(1)
self.query_tokens = nn.Parameter(
torch.zeros(1, self.multimodal_backbone.bert.config.query_length,
self.multimodal_backbone.bert.config.hidden_size))
self.query_tokens.data.normal_(
mean=0.0,
std=self.multimodal_backbone.bert.config.initializer_range)
# freeze the text backbone
for _, param in self.text_backbone.named_parameters():
param.requires_grad = False
if hasattr(self, 'register_load_state_dict_post_hook'):
self.register_load_state_dict_post_hook(self._ignore_llm_keys_hook)
def forward(
self,
images: torch.Tensor,
data_samples: Optional[List] = None,
mode: str = 'loss',
) -> List[DataSample]:
"""The unified entry for a forward process in both training and test.
The method should accept two modes: "predict" and "loss":
- "predict": Forward and return the predictions, which are fully
processed to a list of :obj:`DataSample`.
- "loss": 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`.
Args:
images (torch.Tensor): pre_processed img tensor (N, C, ...).
data_samples (List[DataSample], optional):
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 == 'loss':
return self.loss(images, data_samples)
elif mode == 'predict':
return self.predict(images, data_samples)
else:
raise RuntimeError(f'Invalid mode "{mode}".')
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.
"""
# 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] * image_embeds.size(0)
opt_tokens = self.tokenizer(
prompt, return_tensors='pt').to(images.device)
input_ids = opt_tokens.input_ids
attention_mask = torch.cat([attns_opt, opt_tokens.attention_mask],
dim=1)
query_embeds = inputs_opt
outputs = self.text_backbone.generate(
input_ids=input_ids,
query_embeds=query_embeds,
attention_mask=attention_mask,
do_sample=False,
top_p=0.9,
temperature=1.,
num_beams=5,
max_new_tokens=self.max_txt_len,
min_length=1,
eos_token_id=self.eos_token_id,
repetition_penalty=1.0,
length_penalty=1.0,
num_return_sequences=self.num_captions,
)
output_text = self.tokenizer.batch_decode(
outputs[:, self.prompt_length:], skip_special_tokens=True)
output_text = [text.strip() for text in output_text]
out_data_samples = []
if data_samples is None:
data_samples = [None for _ in range(len(output_text))]
for data_sample, decode_token in zip(data_samples, output_text):
if data_sample is None:
data_sample = DataSample()
data_sample.pred_caption = decode_token
out_data_samples.append(data_sample)
return out_data_samples
@staticmethod
def _ignore_llm_keys_hook(module, incompatible_keys):
"""Avoid warning missing keys of the LLM model."""
import re
llm_pattern = '^text_backbone'
for key in list(incompatible_keys.missing_keys):
if re.match(llm_pattern, key):
incompatible_keys.missing_keys.remove(key)
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