Spaces:
Running
on
Zero
Running
on
Zero
File size: 14,580 Bytes
df6c67d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 |
from time import perf_counter
from typing import Any, Dict, List, Tuple, Union
import clip
import numpy as np
import onnxruntime
from PIL import Image
from inference.core.entities.requests.clip import (
ClipCompareRequest,
ClipImageEmbeddingRequest,
ClipInferenceRequest,
ClipTextEmbeddingRequest,
)
from inference.core.entities.requests.inference import InferenceRequestImage
from inference.core.entities.responses.clip import (
ClipCompareResponse,
ClipEmbeddingResponse,
)
from inference.core.entities.responses.inference import InferenceResponse
from inference.core.env import (
CLIP_MAX_BATCH_SIZE,
CLIP_MODEL_ID,
ONNXRUNTIME_EXECUTION_PROVIDERS,
REQUIRED_ONNX_PROVIDERS,
TENSORRT_CACHE_PATH,
)
from inference.core.exceptions import OnnxProviderNotAvailable
from inference.core.models.roboflow import OnnxRoboflowCoreModel
from inference.core.models.types import PreprocessReturnMetadata
from inference.core.utils.image_utils import load_image_rgb
from inference.core.utils.onnx import get_onnxruntime_execution_providers
from inference.core.utils.postprocess import cosine_similarity
class Clip(OnnxRoboflowCoreModel):
"""Roboflow ONNX ClipModel model.
This class is responsible for handling the ONNX ClipModel model, including
loading the model, preprocessing the input, and performing inference.
Attributes:
visual_onnx_session (onnxruntime.InferenceSession): ONNX Runtime session for visual inference.
textual_onnx_session (onnxruntime.InferenceSession): ONNX Runtime session for textual inference.
resolution (int): The resolution of the input image.
clip_preprocess (function): Function to preprocess the image.
"""
def __init__(
self,
*args,
model_id: str = CLIP_MODEL_ID,
onnxruntime_execution_providers: List[
str
] = get_onnxruntime_execution_providers(ONNXRUNTIME_EXECUTION_PROVIDERS),
**kwargs,
):
"""Initializes the Clip with the given arguments and keyword arguments."""
self.onnxruntime_execution_providers = onnxruntime_execution_providers
t1 = perf_counter()
super().__init__(*args, model_id=model_id, **kwargs)
# Create an ONNX Runtime Session with a list of execution providers in priority order. ORT attempts to load providers until one is successful. This keeps the code across devices identical.
self.log("Creating inference sessions")
self.visual_onnx_session = onnxruntime.InferenceSession(
self.cache_file("visual.onnx"),
providers=self.onnxruntime_execution_providers,
)
self.textual_onnx_session = onnxruntime.InferenceSession(
self.cache_file("textual.onnx"),
providers=self.onnxruntime_execution_providers,
)
if REQUIRED_ONNX_PROVIDERS:
available_providers = onnxruntime.get_available_providers()
for provider in REQUIRED_ONNX_PROVIDERS:
if provider not in available_providers:
raise OnnxProviderNotAvailable(
f"Required ONNX Execution Provider {provider} is not availble. Check that you are using the correct docker image on a supported device."
)
self.resolution = self.visual_onnx_session.get_inputs()[0].shape[2]
self.clip_preprocess = clip.clip._transform(self.resolution)
self.log(f"CLIP model loaded in {perf_counter() - t1:.2f} seconds")
self.task_type = "embedding"
def compare(
self,
subject: Any,
prompt: Any,
subject_type: str = "image",
prompt_type: Union[str, List[str], Dict[str, Any]] = "text",
**kwargs,
) -> Union[List[float], Dict[str, float]]:
"""
Compares the subject with the prompt to calculate similarity scores.
Args:
subject (Any): The subject data to be compared. Can be either an image or text.
prompt (Any): The prompt data to be compared against the subject. Can be a single value (image/text), list of values, or dictionary of values.
subject_type (str, optional): Specifies the type of the subject data. Must be either "image" or "text". Defaults to "image".
prompt_type (Union[str, List[str], Dict[str, Any]], optional): Specifies the type of the prompt data. Can be "image", "text", list of these types, or a dictionary containing these types. Defaults to "text".
**kwargs: Additional keyword arguments.
Returns:
Union[List[float], Dict[str, float]]: A list or dictionary containing cosine similarity scores between the subject and prompt(s). If prompt is a dictionary, returns a dictionary with keys corresponding to the original prompt dictionary's keys.
Raises:
ValueError: If subject_type or prompt_type is neither "image" nor "text".
ValueError: If the number of prompts exceeds the maximum batch size.
"""
if subject_type == "image":
subject_embeddings = self.embed_image(subject)
elif subject_type == "text":
subject_embeddings = self.embed_text(subject)
else:
raise ValueError(
"subject_type must be either 'image' or 'text', but got {request.subject_type}"
)
if isinstance(prompt, dict) and not ("type" in prompt and "value" in prompt):
prompt_keys = prompt.keys()
prompt = [prompt[k] for k in prompt_keys]
prompt_obj = "dict"
else:
prompt = prompt
if not isinstance(prompt, list):
prompt = [prompt]
prompt_obj = "list"
if len(prompt) > CLIP_MAX_BATCH_SIZE:
raise ValueError(
f"The maximum number of prompts that can be compared at once is {CLIP_MAX_BATCH_SIZE}"
)
if prompt_type == "image":
prompt_embeddings = self.embed_image(prompt)
elif prompt_type == "text":
prompt_embeddings = self.embed_text(prompt)
else:
raise ValueError(
"prompt_type must be either 'image' or 'text', but got {request.prompt_type}"
)
similarities = [
cosine_similarity(subject_embeddings, p) for p in prompt_embeddings
]
if prompt_obj == "dict":
similarities = dict(zip(prompt_keys, similarities))
return similarities
def make_compare_response(
self, similarities: Union[List[float], Dict[str, float]]
) -> ClipCompareResponse:
"""
Creates a ClipCompareResponse object from the provided similarity data.
Args:
similarities (Union[List[float], Dict[str, float]]): A list or dictionary containing similarity scores.
Returns:
ClipCompareResponse: An instance of the ClipCompareResponse with the given similarity scores.
Example:
Assuming `ClipCompareResponse` expects a dictionary of string-float pairs:
>>> make_compare_response({"image1": 0.98, "image2": 0.76})
ClipCompareResponse(similarity={"image1": 0.98, "image2": 0.76})
"""
response = ClipCompareResponse(similarity=similarities)
return response
def embed_image(
self,
image: Any,
**kwargs,
) -> np.ndarray:
"""
Embeds an image or a list of images using the Clip model.
Args:
image (Any): The image or list of images to be embedded. Image can be in any format that is acceptable by the preproc_image method.
**kwargs: Additional keyword arguments.
Returns:
np.ndarray: The embeddings of the image(s) as a numpy array.
Raises:
ValueError: If the number of images in the list exceeds the maximum batch size.
Notes:
The function measures performance using perf_counter and also has support for ONNX session to get embeddings.
"""
t1 = perf_counter()
if isinstance(image, list):
if len(image) > CLIP_MAX_BATCH_SIZE:
raise ValueError(
f"The maximum number of images that can be embedded at once is {CLIP_MAX_BATCH_SIZE}"
)
imgs = [self.preproc_image(i) for i in image]
img_in = np.concatenate(imgs, axis=0)
else:
img_in = self.preproc_image(image)
onnx_input_image = {self.visual_onnx_session.get_inputs()[0].name: img_in}
embeddings = self.visual_onnx_session.run(None, onnx_input_image)[0]
return embeddings
def predict(self, img_in: np.ndarray, **kwargs) -> Tuple[np.ndarray]:
onnx_input_image = {self.visual_onnx_session.get_inputs()[0].name: img_in}
embeddings = self.visual_onnx_session.run(None, onnx_input_image)[0]
return (embeddings,)
def make_embed_image_response(
self, embeddings: np.ndarray
) -> ClipEmbeddingResponse:
"""
Converts the given embeddings into a ClipEmbeddingResponse object.
Args:
embeddings (np.ndarray): A numpy array containing the embeddings for an image or images.
Returns:
ClipEmbeddingResponse: An instance of the ClipEmbeddingResponse with the provided embeddings converted to a list.
Example:
>>> embeddings_array = np.array([[0.5, 0.3, 0.2], [0.1, 0.9, 0.0]])
>>> make_embed_image_response(embeddings_array)
ClipEmbeddingResponse(embeddings=[[0.5, 0.3, 0.2], [0.1, 0.9, 0.0]])
"""
response = ClipEmbeddingResponse(embeddings=embeddings.tolist())
return response
def embed_text(
self,
text: Union[str, List[str]],
**kwargs,
) -> np.ndarray:
"""
Embeds a text or a list of texts using the Clip model.
Args:
text (Union[str, List[str]]): The text string or list of text strings to be embedded.
**kwargs: Additional keyword arguments.
Returns:
np.ndarray: The embeddings of the text or texts as a numpy array.
Raises:
ValueError: If the number of text strings in the list exceeds the maximum batch size.
Notes:
The function utilizes an ONNX session to compute embeddings and measures the embedding time with perf_counter.
"""
t1 = perf_counter()
if isinstance(text, list):
if len(text) > CLIP_MAX_BATCH_SIZE:
raise ValueError(
f"The maximum number of text strings that can be embedded at once is {CLIP_MAX_BATCH_SIZE}"
)
texts = text
else:
texts = [text]
texts = clip.tokenize(texts).numpy().astype(np.int32)
onnx_input_text = {self.textual_onnx_session.get_inputs()[0].name: texts}
embeddings = self.textual_onnx_session.run(None, onnx_input_text)[0]
return embeddings
def make_embed_text_response(self, embeddings: np.ndarray) -> ClipEmbeddingResponse:
"""
Converts the given text embeddings into a ClipEmbeddingResponse object.
Args:
embeddings (np.ndarray): A numpy array containing the embeddings for a text or texts.
Returns:
ClipEmbeddingResponse: An instance of the ClipEmbeddingResponse with the provided embeddings converted to a list.
Example:
>>> embeddings_array = np.array([[0.8, 0.1, 0.1], [0.4, 0.5, 0.1]])
>>> make_embed_text_response(embeddings_array)
ClipEmbeddingResponse(embeddings=[[0.8, 0.1, 0.1], [0.4, 0.5, 0.1]])
"""
response = ClipEmbeddingResponse(embeddings=embeddings.tolist())
return response
def get_infer_bucket_file_list(self) -> List[str]:
"""Gets the list of files required for inference.
Returns:
List[str]: The list of file names.
"""
return ["textual.onnx", "visual.onnx"]
def infer_from_request(
self, request: ClipInferenceRequest
) -> ClipEmbeddingResponse:
"""Routes the request to the appropriate inference function.
Args:
request (ClipInferenceRequest): The request object containing the inference details.
Returns:
ClipEmbeddingResponse: The response object containing the embeddings.
"""
t1 = perf_counter()
if isinstance(request, ClipImageEmbeddingRequest):
infer_func = self.embed_image
make_response_func = self.make_embed_image_response
elif isinstance(request, ClipTextEmbeddingRequest):
infer_func = self.embed_text
make_response_func = self.make_embed_text_response
elif isinstance(request, ClipCompareRequest):
infer_func = self.compare
make_response_func = self.make_compare_response
else:
raise ValueError(
f"Request type {type(request)} is not a valid ClipInferenceRequest"
)
data = infer_func(**request.dict())
response = make_response_func(data)
response.time = perf_counter() - t1
return response
def make_response(self, embeddings, *args, **kwargs) -> InferenceResponse:
return [self.make_embed_image_response(embeddings)]
def postprocess(
self,
predictions: Tuple[np.ndarray],
preprocess_return_metadata: PreprocessReturnMetadata,
**kwargs,
) -> Any:
return [self.make_embed_image_response(predictions[0])]
def infer(self, image: Any, **kwargs) -> Any:
"""Embeds an image"""
return super().infer(image, **kwargs)
def preproc_image(self, image: InferenceRequestImage) -> np.ndarray:
"""Preprocesses an inference request image.
Args:
image (InferenceRequestImage): The object containing information necessary to load the image for inference.
Returns:
np.ndarray: A numpy array of the preprocessed image pixel data.
"""
pil_image = Image.fromarray(load_image_rgb(image))
preprocessed_image = self.clip_preprocess(pil_image)
img_in = np.expand_dims(preprocessed_image, axis=0)
return img_in.astype(np.float32)
def preprocess(
self, image: Any, **kwargs
) -> Tuple[np.ndarray, PreprocessReturnMetadata]:
return self.preproc_image(image), PreprocessReturnMetadata({})
|