Spaces:
Runtime error
Runtime error
File size: 7,055 Bytes
3b96cb1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional
import torch
from mmengine.model import BaseModel
from mmpretrain.registry import MODELS, TOKENIZER
from mmpretrain.structures import DataSample
@MODELS.register_module()
class BlipCaption(BaseModel):
"""BLIP Caption.
Args:
vision_encoder (dict): Encoder for extracting image features.
decoder_head (dict): The decoder head module to forward and
calculate loss from processed features.
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
"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,
vision_encoder: dict,
decoder_head: 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):
if data_preprocessor is None:
data_preprocessor = {}
if isinstance(data_preprocessor, dict):
data_preprocessor.setdefault('type', 'MultiModalDataPreprocessor')
data_preprocessor = MODELS.build(data_preprocessor)
super(BlipCaption, self).__init__(
init_cfg=init_cfg, data_preprocessor=data_preprocessor)
self.tokenizer = TOKENIZER.build(tokenizer)
self.visual_encoder = MODELS.build(vision_encoder)
self.seq_gen_head = MODELS.build(decoder_head)
self.prompt = prompt
self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1
self.max_txt_len = max_txt_len
self.num_captions = num_captions
def forward(
self,
images: torch.Tensor,
data_samples: Optional[List] = None,
mode: str = 'loss',
):
"""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): Data samples with
additional infos.
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, data_samples=None, **kwargs):
"""Predict captions from a batch of inputs.
Args:
images (torch.Tensor): The input images 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.
"""
# prepare inputs for decoder generation.
image_embeds = self.visual_encoder(images)[0]
image_embeds = torch.repeat_interleave(image_embeds, self.num_captions,
0)
prompt = [self.prompt] * image_embeds.size(0)
prompt = self.tokenizer(
prompt, padding='longest',
return_tensors='pt').to(image_embeds.device)
prompt.input_ids[:, 0] = self.tokenizer.bos_token_id
prompt.input_ids = prompt.input_ids[:, :-1]
decoder_out = self.seq_gen_head.predict(
input_ids=prompt.input_ids,
encoder_hidden_states=image_embeds,
sep_token_id=self.tokenizer.sep_token_id,
pad_token_id=self.tokenizer.pad_token_id,
output_attentions=True,
return_dict_in_generate=True,
)
decode_tokens = self.tokenizer.batch_decode(
decoder_out.sequences, skip_special_tokens=True)
out_data_samples = []
if data_samples is None:
data_samples = [None for _ in range(len(decode_tokens))]
for data_sample, decode_token in zip(data_samples, decode_tokens):
if data_sample is None:
data_sample = DataSample()
data_sample.pred_caption = decode_token[len(self.prompt):]
out_data_samples.append(data_sample)
return out_data_samples
def loss(self, images, data_samples):
"""Calculate losses from a batch of images and data samples.
Args:
images (torch.Tensor): The input images tensor with shape
(N, C, ...) in general.
data_samples (List[ImageTextDataSample]): The annotation data of
every samples.
Returns:
dict[str, Tensor]: a dictionary of loss components.
"""
image_embeds = self.visual_encoder(images)[0]
raw_text = [self.prompt + ds.gt_caption for ds in data_samples]
text = self.tokenizer(
raw_text,
padding='longest',
truncation=True,
max_length=self.max_txt_len,
return_tensors='pt',
).to(image_embeds.device)
text.input_ids[:, 0] = self.tokenizer.bos_token_id
# prepare targets for forwarding decoder
labels = text.input_ids.masked_fill(
text.input_ids == self.tokenizer.pad_token_id, -100)
labels[:, :self.prompt_length] = -100
# forward decoder
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(image_embeds.device)
losses = self.seq_gen_head.loss(
input_ids=text.input_ids,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
labels=labels,
)
return losses
|