diff --git a/libs/client_infinity/run_tests_with_hook.sh b/libs/client_infinity/run_tests_with_hook.sh old mode 100755 new mode 100644 diff --git a/libs/infinity_emb/infinity_emb/engine.py b/libs/infinity_emb/infinity_emb/engine.py index 4856ff22..210d76ef 100644 --- a/libs/infinity_emb/infinity_emb/engine.py +++ b/libs/infinity_emb/infinity_emb/engine.py @@ -2,9 +2,10 @@ # Copyright (c) 2023-now michaelfeilfeil from asyncio import Semaphore -from typing import Iterable, Iterator, Optional, Set, Union +from typing import Iterable, Iterator, Optional, Set, Union, List from infinity_emb.args import EngineArgs +from infinity_emb.primitives import ImageClassType # prometheus from infinity_emb.inference import ( @@ -202,12 +203,12 @@ async def classify( return scores, usage async def image_embed( - self, *, images: list[str] + self, *, images: List[Union[str, "ImageClassType"]] ) -> tuple[list[EmbeddingReturnType], int]: """embed multiple images Kwargs: - images (list[str]): list of image urls, to be embedded + images (list[Union[str, ImageClassType]]): list of image urls or ImageClassType objects, to be embedded Raises: ValueError: raised if engine is not started yet @@ -339,13 +340,13 @@ async def classify( return await self[model].classify(sentences=sentences, raw_scores=raw_scores) async def image_embed( - self, *, model: str, images: list[str] + self, *, model: str, images: List[Union[str, "ImageClassType"]] ) -> tuple[list[EmbeddingReturnType], int]: """embed multiple images Kwargs: model (str): model name to be used - images (list[str]): list of image urls, to be embedded + images (list[Union[str, ImageClassType]]): list of image urls or ImageClassType objects, to be embedded Raises: ValueError: raised if engine is not started yet diff --git a/libs/infinity_emb/infinity_emb/inference/batch_handler.py b/libs/infinity_emb/infinity_emb/inference/batch_handler.py index 9b8fdf97..8ad12d18 100644 --- a/libs/infinity_emb/infinity_emb/inference/batch_handler.py +++ b/libs/infinity_emb/infinity_emb/inference/batch_handler.py @@ -9,7 +9,7 @@ import time from concurrent.futures import ThreadPoolExecutor from queue import Queue -from typing import Any, Sequence, Set +from typing import Any, Sequence, Set, Union, List import numpy as np @@ -30,6 +30,7 @@ PrioritizedQueueItem, ReRankSingle, get_inner_item, + ImageClassType, ) from infinity_emb.transformer.abstract import BaseTransformer from infinity_emb.transformer.utils import get_lengths_with_tokenize @@ -210,12 +211,12 @@ async def classify( async def image_embed( self, *, - images: list[str], + images: List[Union[str, "ImageClassType"]], ) -> tuple[list[EmbeddingReturnType], int]: """Schedule a images and sentences to be embedded. Awaits until embedded. Args: - images (list[str]): list of pre-signed urls + images (list[Union[str, ImageClassType]]): list of pre-signed urls or ImageClassType objects Raises: ModelNotDeployedError: If loaded model does not expose `embed` diff --git a/libs/infinity_emb/infinity_emb/primitives.py b/libs/infinity_emb/infinity_emb/primitives.py index 96bad0f9..fcbfb24e 100644 --- a/libs/infinity_emb/infinity_emb/primitives.py +++ b/libs/infinity_emb/infinity_emb/primitives.py @@ -26,13 +26,22 @@ TypedDict, TypeVar, Union, + Any, ) import numpy as np import numpy.typing as npt + +EmptyImageClassType: Any = Any if TYPE_CHECKING: - from PIL.Image import Image as ImageClass + try: + from PIL.Image import Image as ImageClass + + EmptyImageClassType = ImageClass + except ImportError: + pass +ImageClassType = EmptyImageClassType # if python>=3.10 use kw_only diff --git a/libs/infinity_emb/infinity_emb/sync_engine.py b/libs/infinity_emb/infinity_emb/sync_engine.py index b1f35706..c168b4e5 100644 --- a/libs/infinity_emb/infinity_emb/sync_engine.py +++ b/libs/infinity_emb/infinity_emb/sync_engine.py @@ -6,10 +6,19 @@ import weakref from concurrent.futures import Future from functools import partial -from typing import TYPE_CHECKING, Awaitable, Callable, Iterator, TypeVar +from typing import ( + TYPE_CHECKING, + Awaitable, + Callable, + Iterator, + TypeVar, + Union, + List, +) from infinity_emb.engine import AsyncEmbeddingEngine, AsyncEngineArray, EngineArgs from infinity_emb.log_handler import logger +from infinity_emb.primitives import ImageClassType if TYPE_CHECKING: from infinity_emb import AsyncEmbeddingEngine @@ -184,7 +193,7 @@ def classify(self, *, model: str, sentences: list[str], raw_scores: bool = False ) @add_start_docstrings(AsyncEngineArray.image_embed.__doc__) - def image_embed(self, *, model: str, images: list[str]): + def image_embed(self, *, model: str, images: List[Union[str, ImageClassType]]): """sync interface of AsyncEngineArray""" return self.async_run( self.async_engine_array.image_embed, model=model, images=images diff --git a/libs/infinity_emb/infinity_emb/transformer/vision/utils.py b/libs/infinity_emb/infinity_emb/transformer/vision/utils.py index 501a0ced..9120dd03 100644 --- a/libs/infinity_emb/infinity_emb/transformer/vision/utils.py +++ b/libs/infinity_emb/infinity_emb/transformer/vision/utils.py @@ -2,25 +2,59 @@ # Copyright (c) 2023-now michaelfeil from infinity_emb._optional_imports import CHECK_PIL, CHECK_REQUESTS -from infinity_emb.primitives import ImageCorruption, ImageSingle +from infinity_emb.primitives import ImageCorruption, ImageSingle, ImageClassType +from typing import Union, List if CHECK_PIL.is_available: from PIL import Image # type: ignore + if CHECK_REQUESTS.is_available: import requests # type: ignore -def resolve_images(image_urls: list[str]) -> list[ImageSingle]: - """Resolve images from URLs.""" - # TODO: improve parallel requests, safety, error handling - CHECK_REQUESTS.mark_required() - CHECK_PIL.mark_required() +def resolve_from_img_obj(img_obj: "ImageClassType") -> ImageSingle: + """Resolve an image from a ImageClassType Object.""" + return ImageSingle(image=img_obj) + +def resolve_from_img_url(img_url: str) -> ImageSingle: + """Resolve an image from an URL.""" try: - downloaded = [requests.get(url, stream=True).raw for url in image_urls] + downloaded_img = requests.get(img_url, stream=True).raw except Exception as e: - raise ImageCorruption(f"Error downloading images: {e}") + raise ImageCorruption(f"error downloading image from url: {e}") + try: - return [ImageSingle(image=Image.open(content)) for content in downloaded] + return ImageSingle(image=Image.open(downloaded_img)) except Exception as e: - raise ImageCorruption(f"Error opening images: {e}") + raise ImageCorruption(f"error opening image from url: {e}") + + +def resolve_image(img: Union[str, "ImageClassType"]) -> ImageSingle: + """Resolve a single image.""" + if isinstance(img, Image.Image): + return resolve_from_img_obj(img) + elif isinstance(img, str): + return resolve_from_img_url(img) + else: + raise ValueError( + f"Invalid image type: {img} is neither str nor ImageClassType object" + ) + + +def resolve_images(images: List[Union[str, "ImageClassType"]]) -> List[ImageSingle]: + """Resolve images from URLs or ImageClassType Objects using multithreading.""" + # TODO: improve parallel requests, safety, error handling + CHECK_REQUESTS.mark_required() + CHECK_PIL.mark_required() + + resolved_imgs = [] + for img in images: + try: + resolved_imgs.append(resolve_image(img)) + except Exception as e: + raise ImageCorruption( + f"Failed to resolve image: {img}.\nError msg: {str(e)}" + ) + + return resolved_imgs diff --git a/libs/infinity_emb/poetry.lock b/libs/infinity_emb/poetry.lock index 9ffc2677..2a022a36 100644 --- a/libs/infinity_emb/poetry.lock +++ b/libs/infinity_emb/poetry.lock @@ -1,91 +1,118 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by 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"diskcache", "einops", "fastapi", "optimum", "orjson", "pillow", "prometheus-fastapi-instrumentator", "pydantic", "rich", "sentence-transformers", "timm", "torch", "typer", "uvicorn"] @@ -4548,4 +4637,4 @@ vision = ["pillow", "timm"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<4" -content-hash = "1435b5c7381f6f35e8390d89d7a122faaed59a17b15507b85e92b61061e1044d" +content-hash = "6ce4f52a3bee90edb74646b590ff30303a058a073b8b09a323e5407c100fa641" diff --git a/libs/infinity_emb/pyproject.toml b/libs/infinity_emb/pyproject.toml index d13e99bd..7f3e1d9d 100644 --- a/libs/infinity_emb/pyproject.toml +++ b/libs/infinity_emb/pyproject.toml @@ -53,6 +53,8 @@ anyio = "*" trio = "*" coverage = {extras = ["toml"], version = "^7.3.2"} mypy = "^1.5.1" +requests = "2.28.1" +types-requests = "2.28.1" # preferred dev dependencies torch = "2.4.0" diff --git a/libs/infinity_emb/tests/unit_test/test_engine.py b/libs/infinity_emb/tests/unit_test/test_engine.py index 89b1a3df..e63ea5b0 100644 --- a/libs/infinity_emb/tests/unit_test/test_engine.py +++ b/libs/infinity_emb/tests/unit_test/test_engine.py @@ -6,6 +6,8 @@ import pytest import torch from sentence_transformers import CrossEncoder # type: ignore[import-untyped] +from PIL import Image +import requests from infinity_emb import AsyncEmbeddingEngine, AsyncEngineArray, EngineArgs from infinity_emb.primitives import ( @@ -184,6 +186,50 @@ async def test_torch_clip_embed(): ) +@pytest.mark.anyio +async def test_clip_embed_pil_image_input(): + img_url = "https://github.com/michaelfeil/infinity/raw/65afe2b3d68fda10429bf7f215fe645be20788e4/docs/assets/cats_coco_sample.jpg" + response = requests.get(img_url, stream=True) + assert response.status_code == 200 + img_data = response.raw + img_obj = Image.open(img_data) + images = [img_obj] # a photo of two cats + sentences = [ + "a photo of two cats", + "a photo of a cat", + "a photo of a dog", + "a photo of a car", + ] + engine = AsyncEmbeddingEngine.from_args( + EngineArgs( + model_name_or_path="wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M", + engine=InferenceEngine.torch, + model_warmup=True, + ) + ) + async with engine: + t1, t2 = asyncio.create_task( + engine.embed(sentences=sentences) + ), asyncio.create_task(engine.image_embed(images=images)) + emb_text, usage_text = await t1 + emb_image, usage_image = await t2 + emb_text_np = np.array(emb_text) # type: ignore + emb_image_np = np.array(emb_image) # type: ignore + + assert emb_text_np.shape[0] == len(sentences) + assert emb_image_np.shape[0] == len(images) + assert emb_text_np.shape[1] >= 10 + assert emb_image_np.shape == emb_image_np[: len(images)].shape + + assert usage_text == sum([len(s) for s in sentences]) + + # check if cat image and two cats are most similar + for i in range(1, len(sentences)): + assert np.dot(emb_text_np[0], emb_image_np[0]) > np.dot( + emb_text_np[i], emb_image_np[0] + ) + + @pytest.mark.anyio @pytest.mark.skipif(sys.platform != "linux", reason="only run these on Linux") @pytest.mark.parametrize(