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import base64
import json
import os
from io import BytesIO
from typing import Any, Dict, List, Literal, Optional, Union
import requests
import torch
from PIL import Image
from torch import nn
from transformers import AutoConfig, AutoImageProcessor, AutoModel, AutoTokenizer
class Transformer(nn.Module):
save_in_root: bool = True
def __init__(
self,
model_name_or_path: str = 'jinaai/jina-clip-v2',
tokenizer_name_or_path: Optional[str] = None,
image_processor_name_or_path: Optional[str] = None,
max_seq_length: Optional[int] = None,
config_args: Optional[Dict[str, Any]] = None,
model_args: Optional[Dict[str, Any]] = None,
tokenizer_args: Optional[Dict[str, Any]] = None,
image_processor_args: Optional[Dict[str, Any]] = None,
assume_text_inputs: bool = False,
cache_dir: Optional[str] = None,
backend: Literal['torch', 'onnx', 'openvino'] = 'torch',
**_,
) -> None:
"""
Creates a custom SentenceTransformer module that uses `jinai/jina-clip-v2` to
map sentences/images to embeddings
Args:
model_name_or_path (str, optional): If it is a filepath on disc, it loads
the model from that path. If it is not a path, tries to construct a
model from the Hugging Face Hub with that name. Defaults to
'jinaai/jina-clip-v2'
tokenizer_name_or_path (str, optional): If it is a filepath on disc, it
loads the tokenizer from that path. If it is not a path, tries to
construct a tokenizer from the Hugging Face Hub with that name.
If `None` it is automatically set to the value of `model_name_or_path`
image_processor_name_or_path (str, optional): If it is a filepath on disc,
it loads the image processor from that path. If it is not a path, tries
to construct an image processor from the Hugging Face Hub with that
name. If `None` it is automatically set to the value of
`model_name_or_path`
max_seq_length (int, optional): The maximum sequence length of the model.
If not provided, will be inferred from model or tokenizer
config_args (Dict[str, Any], optional): Additional model configuration
parameters to be passed to the Hugging Face Transformers config
model_args (Dict[str, Any], optional): Additional model configuration
parameters to be passed to the Hugging Face Transformers model
tokenizer_args (Dict[str, Any], optional): Additional tokenizer
configuration parameters to be passed to the Hugging Face Transformers
tokenizer
image_processor_args (Dict[str, Any], optional): Additional image processor
configuration parameters to be passed to the Hugging Face Transformers
image processor
assume_text_inputs (bool, optional): If set to `True`, all inputs are
treated as texts. Defaults to `False`
cache_dir (str, optional): The Hugging Face Hub cache directory
backend (str, optional): Computational backend, only 'torch' is supported
Example:
::
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(
'jinaai/jina-clip-v2', trust_remote_code=True
)
sentences_or_images = [
"The weather is lovely today.",
"It's so sunny outside!",
"/path/to/stadium.jpg",
]
embeddings = model.encode(sentences_or_images)
print(embeddings.shape)
# (3, 1024)
# Get the similarity scores between all inputs
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6817, 0.0492],
# [0.6817, 1.0000, 0.0421],
# [0.0492, 0.0421, 1.0000]])
"""
super(Transformer, self).__init__()
if backend != 'torch':
raise ValueError(
f'Backend \'{backend}\' is not supported, please use \'torch\' instead'
)
config_kwargs = config_args or {}
model_kwargs = model_args or {}
tokenizer_kwargs = tokenizer_args or {}
image_processor_kwargs = {
'token': model_kwargs.get('token', None),
'trust_remote_code': model_kwargs.get('trust_remote_code', False),
'revision': model_kwargs.get('revision', None),
'local_files_only': model_kwargs.get('local_files_only', None),
}
image_processor_kwargs.update(image_processor_args or {})
config = AutoConfig.from_pretrained(
model_name_or_path, cache_dir=cache_dir, **config_kwargs
)
self.model = AutoModel.from_pretrained(
model_name_or_path, config=config, cache_dir=cache_dir, **model_kwargs
)
if max_seq_length is not None and 'model_max_length' not in tokenizer_kwargs:
tokenizer_kwargs['model_max_length'] = max_seq_length
self.tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name_or_path or model_name_or_path,
cache_dir=cache_dir,
**tokenizer_kwargs,
)
self.image_processor = AutoImageProcessor.from_pretrained(
image_processor_name_or_path or model_name_or_path,
cache_dir=cache_dir,
**image_processor_kwargs,
)
self.assume_text_inputs = assume_text_inputs
# No max_seq_length set. Try to infer from model
if max_seq_length is None:
if (
hasattr(self.model, 'config')
and hasattr(self.model.config, 'max_position_embeddings')
and hasattr(self.tokenizer, 'model_max_length')
):
max_seq_length = min(
self.model.config.max_position_embeddings,
self.tokenizer.model_max_length,
)
self.max_seq_length = max_seq_length
if tokenizer_name_or_path is not None:
self.model.config.tokenizer_class = self.tokenizer.__class__.__name__
@staticmethod
def _decode_data_image(data_image_str: str) -> Image.Image:
header, data = data_image_str.split(',', 1)
image_data = base64.b64decode(data)
return Image.open(BytesIO(image_data))
def tokenize(
self, texts: List[Union[str, Image.Image]], padding: Union[str, bool] = True
) -> Dict[str, torch.Tensor]:
"""
Encodes input samples. Text samples are tokenized. Image URLs, image data
buffers and PIL images are passed through the image processor.
"""
_images = []
_texts = []
_image_or_text_descriptors = []
if self.assume_text_inputs:
for sample in texts:
if isinstance(sample, str):
_texts.append(sample)
_image_or_text_descriptors.append(1)
else:
for sample in texts:
if isinstance(sample, str):
if sample.startswith('http'):
try:
response = requests.get(sample)
_images.append(
Image.open(BytesIO(response.content)).convert('RGB')
)
_image_or_text_descriptors.append(0)
except Exception as e:
_ = str(e)
_texts.append(sample)
_image_or_text_descriptors.append(1)
elif sample.startswith('data:image/'):
_images.append(self._decode_data_image(sample).convert('RGB'))
_image_or_text_descriptors.append(0)
else:
try:
_images.append(Image.open(sample).convert('RGB'))
_image_or_text_descriptors.append(0)
except Exception as e:
_ = str(e)
_texts.append(sample)
_image_or_text_descriptors.append(1)
elif isinstance(sample, Image.Image):
_images.append(sample.convert('RGB'))
_image_or_text_descriptors.append(0)
encoding = {}
if len(_texts):
encoding['input_ids'] = self.tokenizer(
_texts,
padding=padding,
truncation='longest_first',
return_tensors='pt',
max_length=self.max_seq_length,
).input_ids
if len(_images):
encoding['pixel_values'] = self.image_processor(
_images, return_tensors='pt'
).pixel_values
encoding['image_text_info'] = _image_or_text_descriptors
return encoding
def forward(self, features: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
image_embeddings = []
text_embeddings = []
if 'pixel_values' in features:
image_embeddings = self.model.get_image_features(features['pixel_values'])
if 'input_ids' in features:
text_embeddings = self.model.get_text_features(features['input_ids'])
sentence_embedding = []
image_features = iter(image_embeddings)
text_features = iter(text_embeddings)
for _, _input_type in enumerate(features['image_text_info']):
if _input_type == 0:
sentence_embedding.append(next(image_features))
else:
sentence_embedding.append(next(text_features))
features['sentence_embedding'] = torch.stack(sentence_embedding).float()
return features
def save(self, output_path: str, safe_serialization: bool = True) -> None:
self.model.save_pretrained(output_path, safe_serialization=safe_serialization)
self.tokenizer.save_pretrained(output_path)
self.image_processor.save_pretrained(output_path)
@staticmethod
def load(input_path: str) -> 'Transformer':
# Old classes used other config names than 'sentence_bert_config.json'
for config_name in [
'sentence_bert_config.json',
'sentence_roberta_config.json',
'sentence_distilbert_config.json',
'sentence_camembert_config.json',
'sentence_albert_config.json',
'sentence_xlm-roberta_config.json',
'sentence_xlnet_config.json',
]:
sbert_config_path = os.path.join(input_path, config_name)
if os.path.exists(sbert_config_path):
break
with open(sbert_config_path) as fIn:
config = json.load(fIn)
# Don't allow configs to set trust_remote_code
if 'config_kwargs' in config and 'trust_remote_code' in config['config_kwargs']:
config['config_kwargs'].pop('trust_remote_code')
if 'model_kwargs' in config and 'trust_remote_code' in config['model_kwargs']:
config['model_kwargs'].pop('trust_remote_code')
if (
'tokenizer_kwargs' in config
and 'trust_remote_code' in config['tokenizer_kwargs']
):
config['tokenizer_kwargs'].pop('trust_remote_code')
if (
'image_processor_kwargs' in config
and 'trust_remote_code' in config['image_processor_kwargs']
):
config['image_processor_kwargs'].pop('trust_remote_code')
return Transformer(model_name_or_path=input_path, **config)
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