File size: 15,291 Bytes
3943768 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 |
"""
Based upon ImageCaptionLoader in LangChain version: langchain/document_loaders/image_captions.py
But accepts preloaded model to avoid slowness in use and CUDA forking issues
Loader that loads image captions
By default, the loader utilizes the pre-trained image captioning model.
https://huggingface.co/microsoft/Florence-2-base
"""
from typing import List, Union, Any, Tuple
import requests
from langchain.docstore.document import Document
from langchain_community.document_loaders import ImageCaptionLoader
from utils import get_device, NullContext, clear_torch_cache
from importlib.metadata import distribution, PackageNotFoundError
try:
assert distribution('bitsandbytes') is not None
have_bitsandbytes = True
except (PackageNotFoundError, AssertionError):
have_bitsandbytes = False
from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple, Union
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class ImageCaptionLoader(BaseLoader):
"""Load image captions.
By default, the loader utilizes the pre-trained
Salesforce BLIP image captioning model.
https://huggingface.co/Salesforce/blip-image-captioning-base
"""
def __init__(
self,
images: Union[str, Path, bytes, List[Union[str, bytes, Path]]],
caption_processor: str = "Salesforce/blip-image-captioning-base",
caption_model: str = "Salesforce/blip-image-captioning-base",
):
"""Initialize with a list of image data (bytes) or file paths
Args:
images: Either a single image or a list of images. Accepts
image data (bytes) or file paths to images.
caption_processor: The name of the pre-trained BLIP processor.
caption_model: The name of the pre-trained BLIP model.
"""
if isinstance(images, (str, Path, bytes)):
self.images = [images]
else:
self.images = images
self.caption_processor = caption_processor
self.caption_model = caption_model
def load(self) -> List[Document]:
"""Load from a list of image data or file paths"""
try:
from transformers import BlipForConditionalGeneration, BlipProcessor
except ImportError:
raise ImportError(
"`transformers` package not found, please install with "
"`pip install transformers`."
)
processor = BlipProcessor.from_pretrained(self.caption_processor)
model = BlipForConditionalGeneration.from_pretrained(self.caption_model)
results = []
for image in self.images:
caption, metadata = self._get_captions_and_metadata(
model=model, processor=processor, image=image
)
doc = Document(page_content=caption, metadata=metadata)
results.append(doc)
return results
def _get_captions_and_metadata(
self, model: Any, processor: Any, image: Union[str, Path, bytes]
) -> Tuple[str, dict]:
"""Helper function for getting the captions and metadata of an image."""
try:
from PIL import Image
except ImportError:
raise ImportError(
"`PIL` package not found, please install with `pip install pillow`"
)
image_source = image # Save the original source for later reference
try:
if isinstance(image, bytes):
image = Image.open(BytesIO(image)).convert("RGB")
elif isinstance(image, str) and (
image.startswith("http://") or image.startswith("https://")
):
image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
else:
image = Image.open(image).convert("RGB")
except Exception:
if isinstance(image_source, bytes):
msg = "Could not get image data from bytes"
else:
msg = f"Could not get image data for {image_source}"
raise ValueError(msg)
inputs = processor(image, "an image of", return_tensors="pt")
output = model.generate(**inputs)
caption: str = processor.decode(output[0])
if isinstance(image_source, bytes):
metadata: dict = {"image_source": "Image bytes provided"}
else:
metadata = {"image_path": str(image_source)}
return caption, metadata
class H2OImageCaptionLoader(ImageCaptionLoader):
"""Loader that loads the captions of an image"""
def __init__(self, path_images: Union[str, List[str]] = None,
caption_processor: str = None,
caption_model: str = None,
caption_gpu=True,
load_in_8bit=True,
# True doesn't seem to work, even though https://huggingface.co/Salesforce/blip2-flan-t5-xxl#in-8-bit-precision-int8
load_half=False,
load_gptq='',
load_awq='',
load_exllama=False,
use_safetensors=False,
revision=None,
min_new_tokens=512,
max_tokens=50,
gpu_id='auto'):
if caption_model is None or caption_model is None:
caption_processor = "microsoft/Florence-2-base"
caption_model = "microsoft/Florence-2-base"
super().__init__(path_images, caption_processor, caption_model)
self.caption_processor = caption_processor
self.caption_model = caption_model
self.processor = None
self.model = None
self.caption_gpu = caption_gpu
self.context_class = NullContext
self.load_in_8bit = load_in_8bit and have_bitsandbytes # only for blip2
self.load_half = load_half
self.load_gptq = load_gptq
self.load_awq = load_awq
self.load_exllama = load_exllama
self.use_safetensors = use_safetensors
self.revision = revision
self.gpu_id = gpu_id
# default prompt
self.prompt = "image of"
self.min_new_tokens = min_new_tokens
self.max_tokens = max_tokens
self.device = 'cpu'
self.device_map = {"": 'cpu'}
self.set_context()
def set_context(self):
if get_device() == 'cuda' and self.caption_gpu:
import torch
n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
if n_gpus > 0:
self.context_class = torch.device
self.device = 'cuda'
else:
self.device = 'cpu'
else:
self.device = 'cpu'
if self.caption_gpu:
if self.gpu_id == 'auto':
# blip2 has issues with multi-GPU. Error says need to somehow set language model in device map
# device_map = 'auto'
self.device_map = {"": 0}
else:
if self.device == 'cuda':
self.device_map = {"": 'cuda:%d' % self.gpu_id}
else:
self.device_map = {"": 'cpu'}
else:
self.device_map = {"": 'cpu'}
def load_model(self):
try:
import transformers
except ImportError:
raise ValueError(
"`transformers` package not found, please install with "
"`pip install transformers`."
)
self.set_context()
if self.model:
if not self.load_in_8bit and str(self.model.device) != self.device_map['']:
self.model.to(self.device)
return self
import torch
with torch.no_grad():
with self.context_class(self.device):
context_class_cast = NullContext if self.device == 'cpu' else torch.autocast
with context_class_cast(self.device):
if 'blip2' in self.caption_processor.lower():
from transformers import Blip2Processor, Blip2ForConditionalGeneration
if self.load_half and not self.load_in_8bit:
self.processor = Blip2Processor.from_pretrained(self.caption_processor,
device_map=self.device_map).half()
self.model = Blip2ForConditionalGeneration.from_pretrained(self.caption_model,
device_map=self.device_map).half()
else:
self.processor = Blip2Processor.from_pretrained(self.caption_processor,
load_in_8bit=self.load_in_8bit,
device_map=self.device_map,
)
self.model = Blip2ForConditionalGeneration.from_pretrained(self.caption_model,
load_in_8bit=self.load_in_8bit,
device_map=self.device_map)
elif 'blip' in self.caption_processor.lower():
from transformers import BlipForConditionalGeneration, BlipProcessor
self.load_half = False # not supported
self.processor = BlipProcessor.from_pretrained(self.caption_processor, device_map=self.device_map)
self.model = BlipForConditionalGeneration.from_pretrained(self.caption_model,
device_map=self.device_map)
else:
from transformers import AutoModelForCausalLM, AutoProcessor
self.load_half = False # not supported
self.processor = AutoProcessor.from_pretrained(self.caption_processor, device_map=self.device_map,
trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(self.caption_model, device_map=self.device_map,
trust_remote_code=True)
return self
def set_image_paths(self, path_images: Union[str, List[str]]):
"""
Load from a list of image files
"""
if isinstance(path_images, str):
self.image_paths = [path_images]
else:
self.image_paths = path_images
def load(self, prompt=None) -> List[Document]:
if self.processor is None or self.model is None:
self.load_model()
results = []
for path_image in self.image_paths:
caption, metadata = self._get_captions_and_metadata(
model=self.model, processor=self.processor, path_image=path_image,
prompt=prompt,
)
doc = Document(page_content=caption, metadata=metadata)
results.append(doc)
return results
def unload_model(self):
if hasattr(self, 'model') and hasattr(self.model, 'cpu'):
self.model.cpu()
clear_torch_cache()
def _get_captions_and_metadata(
self, model: Any, processor: Any, path_image: str,
prompt=None) -> Tuple[str, dict]:
"""
Helper function for getting the captions and metadata of an image
"""
if prompt is None:
prompt = self.prompt
try:
from PIL import Image
except ImportError:
raise ValueError(
"`PIL` package not found, please install with `pip install pillow`"
)
try:
if path_image.startswith("http://") or path_image.startswith("https://"):
image = Image.open(requests.get(path_image, stream=True).raw).convert(
"RGB"
)
else:
image = Image.open(path_image).convert("RGB")
except Exception:
raise ValueError(f"Could not get image data for {path_image}")
import torch
with torch.no_grad():
with self.context_class(self.device):
context_class_cast = NullContext if self.device == 'cpu' else torch.autocast
with context_class_cast(self.device):
extra_kwargs = {}
if isinstance(self.caption_model, str) and 'florence' in self.caption_model.lower():
caption_detail_task_map = {
"low": "<CAPTION>",
"medium": "<DETAILED_CAPTION>",
"high": "<MORE_DETAILED_CAPTION>",
}
task_prompt = caption_detail_task_map[
'high' if 'large' in self.caption_model else 'medium'
]
num_beams = 3 if 'large' in self.caption_model else 1
extra_kwargs.update(dict(num_beams=num_beams))
if prompt and False:
prompt = task_prompt + prompt
else:
prompt = task_prompt
if isinstance(self.caption_model, str) and 'blip' in self.caption_model:
min_length = len(prompt) // 4 + self.min_new_tokens
self.max_tokens = max(self.max_tokens, min_length)
extra_kwargs.update(dict(min_length=min_length))
if self.load_half:
# FIXME: RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'
inputs = processor(image, prompt, return_tensors="pt") # .half()
else:
inputs = processor(image, prompt, return_tensors="pt")
else:
inputs = processor(text=prompt, images=image, return_tensors="pt")
inputs.to(model.device)
output = model.generate(**inputs, max_length=self.max_tokens, **extra_kwargs)
caption: str = processor.decode(output[0], skip_special_tokens=True)
if isinstance(self.caption_model, str) and 'blip' in self.caption_model:
prompti = caption.find(prompt)
if prompti >= 0:
caption = caption[prompti + len(prompt):]
elif isinstance(self.caption_model, str) and 'florence' in self.caption_model.lower():
parsed_answer = processor.post_process_generation(
caption, task=task_prompt, image_size=(image.width, image.height)
)
caption: str = parsed_answer[task_prompt].strip()
metadata: dict = {"image_path": path_image}
return caption, metadata
|