File size: 7,488 Bytes
e03d74b 820eec2 e03d74b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import base64
import json
import os
from io import BytesIO
from typing import Any, Dict, List, Optional, Tuple, Union
import requests
import torch
from PIL import Image
from torch import nn
from transformers import AutoConfig, AutoImageProcessor, AutoModel, AutoTokenizer
class Transformer(nn.Module):
"""Huggingface AutoModel to generate token embeddings.
Loads the correct class, e.g. BERT / RoBERTa etc.
Args:
model_name_or_path: Huggingface models name
(https://huggingface.co/models)
max_seq_length: Truncate any inputs longer than max_seq_length
model_args: Keyword arguments passed to the Huggingface
Transformers model
tokenizer_args: Keyword arguments passed to the Huggingface
Transformers tokenizer
config_args: Keyword arguments passed to the Huggingface
Transformers config
cache_dir: Cache dir for Huggingface Transformers to store/load
models
do_lower_case: If true, lowercases the input (independent if the
model is cased or not)
tokenizer_name_or_path: Name or path of the tokenizer. When
None, then model_name_or_path is used
"""
def __init__(
self,
model_name_or_path: str,
max_seq_length: Optional[int] = None,
model_args: Optional[Dict[str, Any]] = None,
tokenizer_args: Optional[Dict[str, Any]] = None,
config_args: Optional[Dict[str, Any]] = None,
cache_dir: Optional[str] = None,
do_lower_case: bool = False,
tokenizer_name_or_path: str = None,
) -> None:
super(Transformer, self).__init__()
self.config_keys = ["max_seq_length", "do_lower_case"]
self.do_lower_case = do_lower_case
if model_args is None:
model_args = {}
if tokenizer_args is None:
tokenizer_args = {}
if config_args is None:
config_args = {}
config = AutoConfig.from_pretrained(
model_name_or_path, **config_args, cache_dir=cache_dir
)
self.jina_clip = AutoModel.from_pretrained(
model_name_or_path, config=config, cache_dir=cache_dir, **model_args
)
if max_seq_length is not None and "model_max_length" not in tokenizer_args:
tokenizer_args["model_max_length"] = max_seq_length
self.tokenizer = AutoTokenizer.from_pretrained(
(
tokenizer_name_or_path
if tokenizer_name_or_path is not None
else model_name_or_path
),
cache_dir=cache_dir,
**tokenizer_args,
)
self.preprocessor = AutoImageProcessor.from_pretrained(
(
tokenizer_name_or_path
if tokenizer_name_or_path is not None
else model_name_or_path
),
cache_dir=cache_dir,
**tokenizer_args,
)
# No max_seq_length set. Try to infer from model
if max_seq_length is None:
if (
hasattr(self.jina_clip, "config")
and hasattr(self.jina_clip.config, "max_position_embeddings")
and hasattr(self.tokenizer, "model_max_length")
):
max_seq_length = min(
self.jina_clip.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.jina_clip.config.tokenizer_class = self.tokenizer.__class__.__name__
def forward(
self, features: Dict[str, torch.Tensor]
) -> Dict[str, torch.Tensor]:
"""Returns token_embeddings, cls_token"""
if "input_ids" in features:
embedding = self.jina_clip.get_text_features(
input_ids=features["input_ids"]
)
else:
embedding = self.jina_clip.get_image_features(
pixel_values=features["pixel_values"]
)
return {"sentence_embedding": embedding}
def get_word_embedding_dimension(self) -> int:
return self.config.text_config.embed_dim
def decode_data_image(data_image_str):
header, data = data_image_str.split(',', 1)
image_data = base64.b64decode(data)
return Image.open(BytesIO(image_data))
def tokenize(
self, batch: Union[List[str]], padding: Union[str, bool] = True
) -> Dict[str, torch.Tensor]:
"""Tokenizes a text and maps tokens to token-ids"""
images = []
texts = []
for sample in batch:
if isinstance(sample, str):
if sample.startswith('http'):
response = requests.get(sample)
images.append(Image.open(BytesIO(response.content)).convert('RGB'))
elif sample.startswith('data:image/'):
images.append(self.decode_data_image(sample).convert('RGB'))
else:
# TODO: Make sure that Image.open fails for non-image files
try:
images.append(Image.open(sample).convert('RGB'))
except:
texts.append(sample)
elif isinstance(sample, Image.Image):
images.append(sample.convert('RGB'))
if images and texts:
raise ValueError('Batch must contain either images or texts, not both')
if texts:
return self.tokenizer(
texts,
padding=padding,
truncation="longest_first",
return_tensors="pt",
max_length=self.max_seq_length,
)
elif images:
return self.preprocessor(images)
return {}
def save(self, output_path: str, safe_serialization: bool = True) -> None:
self.jina_clip.save_pretrained(
output_path, safe_serialization=safe_serialization
)
self.tokenizer.save_pretrained(output_path)
self.preprocessor.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 "model_args" in config and "trust_remote_code" in config["model_args"]:
config["model_args"].pop("trust_remote_code")
if (
"tokenizer_args" in config
and "trust_remote_code" in config["tokenizer_args"]
):
config["tokenizer_args"].pop("trust_remote_code")
if "config_args" in config and "trust_remote_code" in config["config_args"]:
config["config_args"].pop("trust_remote_code")
return Transformer(model_name_or_path=input_path, **config)
|