From e803fe14c2ff64d2c7d800b25fd4a05e28bfea61 Mon Sep 17 00:00:00 2001 From: Asia <92344512+AsiaCao@users.noreply.github.com> Date: Mon, 22 Apr 2024 14:14:35 -0700 Subject: [PATCH] Remove torch and cuda dependencies (#60) Resolve merge conflicts --- docs/api/data.md | 3 - docs/api/emb.md | 3 - mkdocs.yml | 2 - poetry.lock | 793 +--------------------------------- pyproject.toml | 8 +- tests/test_data.py | 93 ---- vision_agent/__init__.py | 2 - vision_agent/data/__init__.py | 1 - vision_agent/data/data.py | 142 ------ vision_agent/emb/__init__.py | 1 - vision_agent/emb/emb.py | 47 -- 11 files changed, 7 insertions(+), 1088 deletions(-) delete mode 100644 docs/api/data.md delete mode 100644 docs/api/emb.md delete mode 100644 tests/test_data.py delete mode 100644 vision_agent/data/__init__.py delete mode 100644 vision_agent/data/data.py delete mode 100644 vision_agent/emb/__init__.py delete mode 100644 vision_agent/emb/emb.py diff --git a/docs/api/data.md b/docs/api/data.md deleted file 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+content-hash = "5c57abb9e41b66ee6195875318971f2c2ce858879efca8f5edefb2226284ee6c" diff --git a/pyproject.toml b/pyproject.toml index 0670265f..9e37d2c1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "vision-agent" -version = "0.1.5" +version = "0.2.0" description = "Toolset for Vision Agent" authors = ["Landing AI "] readme = "README.md" @@ -16,16 +16,12 @@ packages = [{include = "vision_agent"}] "documentation" = "https://github.com/landing-ai/vision-agent" [tool.poetry.dependencies] # main dependency group -python = ">=3.9,<3.12" - +python = ">=3.9" numpy = ">=1.21.0,<2.0.0" pillow = "10.*" requests = "2.*" tqdm = ">=4.64.0,<5.0.0" pandas = "2.*" -faiss-cpu = "1.*" -torch = "2.1.*" # 2.2 causes sentence-transformers to seg fault -sentence-transformers = "2.*" openai = "1.*" typing_extensions = "4.*" moviepy = "1.*" diff --git a/tests/test_data.py b/tests/test_data.py deleted file mode 100644 index 046fc0fb..00000000 --- a/tests/test_data.py +++ /dev/null @@ -1,93 +0,0 @@ -import shutil -from pathlib import Path - -import numpy as np -import pandas as pd -import pytest - -from vision_agent.data import DataStore, build_data_store - - -@pytest.fixture(autouse=True) -def clean_up(): - yield - for p in Path(".").glob("test_save*"): - if p.is_dir(): - shutil.rmtree(p) - else: - p.unlink() - - -@pytest.fixture -def small_ds(): - df = pd.DataFrame({"image_paths": ["path1", "path2"]}) - ds = DataStore(df) - return ds - - -@pytest.fixture -def small_ds_with_index(small_ds): - small_ds.add_embedder(TestEmb()) - small_ds.add_lmm(TestLMM()) - small_ds.add_column("test", "test prompt") - small_ds.build_index("test") - return small_ds - - -class TestLMM: - def generate(self, _, **kwargs): - return "test" - - -class TestEmb: - def embed(self, _): - return np.random.randn(128).astype(np.float32) - - -def test_initialize_data_store(small_ds): - assert small_ds is not None - assert "image_id" in small_ds.df.columns - assert "image_paths" in small_ds.df.columns - - -def test_initialize_data_store_with_no_data(): - df = pd.DataFrame({"x": ["path1"]}) - with pytest.raises(ValueError): - DataStore(df) - - -def test_build_data_store(): - ds = build_data_store(["path1", "path2"]) - assert isinstance(ds, DataStore) - assert "image_id" in ds.df.columns - - -def test_add_index_no_emb(small_ds): - with pytest.raises(ValueError): - small_ds.build_index("test") - - -def test_add_column_no_lmm(small_ds): - with pytest.raises(ValueError): - small_ds.add_column("test", "test prompt") - - -def test_search(small_ds_with_index): - results = small_ds_with_index.search("test", top_k=1) - assert len(results) == 1 - - -def test_save(small_ds_with_index): - small_ds_with_index.save("test_save") - - assert Path("test_save").exists() - assert Path("test_save/data.csv").exists() - assert Path("test_save/data.index").exists() - - -def test_load(small_ds_with_index): - small_ds_with_index.save("test_save") - - second_ds = DataStore.load("test_save") - assert second_ds.df.equals(small_ds_with_index.df) - assert second_ds.index is not None diff --git a/vision_agent/__init__.py b/vision_agent/__init__.py index 360b8a78..bf71560f 100644 --- a/vision_agent/__init__.py +++ b/vision_agent/__init__.py @@ -1,5 +1,3 @@ from .agent import Agent -from .data import DataStore, build_data_store -from .emb import Embedder, OpenAIEmb, SentenceTransformerEmb, get_embedder from .llm import LLM, OpenAILLM from .lmm import LMM, LLaVALMM, OpenAILMM, get_lmm diff --git a/vision_agent/data/__init__.py b/vision_agent/data/__init__.py deleted file mode 100644 index 050da913..00000000 --- a/vision_agent/data/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .data import DataStore, build_data_store diff --git a/vision_agent/data/data.py b/vision_agent/data/data.py deleted file mode 100644 index 54125772..00000000 --- a/vision_agent/data/data.py +++ /dev/null @@ -1,142 +0,0 @@ -from __future__ import annotations - -import uuid -from pathlib import Path -from typing import Callable, Dict, List, Optional, Union, cast - -import faiss -import numpy as np -import numpy.typing as npt -import pandas as pd -from faiss import read_index, write_index -from tqdm import tqdm -from typing_extensions import Self - -from vision_agent.emb import Embedder -from vision_agent.lmm import LMM - -tqdm.pandas() - - -class DataStore: - r"""A class to store and manage image data along with its generated metadata from an LMM.""" - - def __init__(self, df: pd.DataFrame): - r"""Initializes the DataStore with a DataFrame containing image paths and image - IDs. If the image IDs are not present, they are generated using UUID4. The - DataFrame must contain an 'image_paths' column. - - Args: - df: The DataFrame containing "image_paths" and "image_id" columns. - """ - self.df = df - self.lmm: Optional[LMM] = None - self.emb: Optional[Embedder] = None - self.index: Optional[faiss.IndexFlatIP] = None # type: ignore - if "image_paths" not in self.df.columns: - raise ValueError("image_paths column must be present in DataFrame") - if "image_id" not in self.df.columns: - self.df["image_id"] = [str(uuid.uuid4()) for _ in range(len(df))] - - def add_embedder(self, emb: Embedder) -> Self: - self.emb = emb - return self - - def add_lmm(self, lmm: LMM) -> Self: - self.lmm = lmm - return self - - def add_column( - self, name: str, prompt: str, func: Optional[Callable[[str], str]] = None - ) -> Self: - r"""Adds a new column to the DataFrame containing the generated metadata from - the LMM. - - Args: - name: The name of the column to be added. - prompt: The prompt to be used to generate the metadata. - func: A Python function to be applied on the output of `lmm.generate`. - Defaults to None. - """ - if self.lmm is None: - raise ValueError("LMM not set yet") - - self.df[name] = self.df["image_paths"].progress_apply( # type: ignore - lambda x: ( - func(self.lmm.generate(prompt, images=[x])) - if func - else self.lmm.generate(prompt, images=[x]) - ) - ) - return self - - def build_index(self, target_col: str) -> Self: - r"""This will generate embeddings for the `target_col` and build a searchable - index over them, so next time you run search it will search over this index. - - Args: - target_col: The column name containing the data to be indexed.""" - if self.emb is None: - raise ValueError("Embedder not set yet") - - embeddings: pd.Series = self.df[target_col].progress_apply(lambda x: self.emb.embed(x)) # type: ignore - embeddings_np = np.array(embeddings.tolist()).astype(np.float32) - self.index = faiss.IndexFlatIP(embeddings_np.shape[1]) - self.index.add(embeddings_np) - return self - - def get_embeddings(self) -> npt.NDArray[np.float32]: - if self.index is None: - raise ValueError("Index not built yet") - - ntotal = self.index.ntotal - d: int = self.index.d - return cast( - npt.NDArray[np.float32], - faiss.rev_swig_ptr(self.index.get_xb(), ntotal * d).reshape(ntotal, d), - ) - - def search(self, query: str, top_k: int = 10) -> List[Dict]: - r"""Searches the index for the most similar images to the query and returns - the top_k results. - - Args: - query: The query to search for. - top_k: The number of results to return. Defaults to 10.""" - if self.index is None: - raise ValueError("Index not built yet") - if self.emb is None: - raise ValueError("Embedder not set yet") - - query_embedding: npt.NDArray[np.float32] = self.emb.embed(query) - _, idx = self.index.search(query_embedding.reshape(1, -1), top_k) - return cast(List[Dict], self.df.iloc[idx[0]].to_dict(orient="records")) - - def save(self, path: Union[str, Path]) -> None: - path = Path(path) - path.mkdir(parents=True) - self.df.to_csv(path / "data.csv") - if self.index is not None: - write_index(self.index, str(path / "data.index")) - - @classmethod - def load(cls, path: Union[str, Path]) -> DataStore: - path = Path(path) - df = pd.read_csv(path / "data.csv", index_col=0) - ds = DataStore(df) - if Path(path / "data.index").exists(): - ds.index = read_index(str(path / "data.index")) - return ds - - -def build_data_store(data: Union[str, Path, list[Union[str, Path]]]) -> DataStore: - if isinstance(data, Path) or isinstance(data, str): - data = Path(data) - data_files = list(Path(data).glob("*")) - elif isinstance(data, list): - data_files = [Path(d) for d in data] - - df = pd.DataFrame() - df["image_paths"] = data_files - df["image_id"] = [uuid.uuid4() for _ in range(len(data_files))] - return DataStore(df) diff --git a/vision_agent/emb/__init__.py b/vision_agent/emb/__init__.py deleted file mode 100644 index 729b96f8..00000000 --- a/vision_agent/emb/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .emb import Embedder, OpenAIEmb, SentenceTransformerEmb, get_embedder diff --git a/vision_agent/emb/emb.py b/vision_agent/emb/emb.py deleted file mode 100644 index 6186ddb4..00000000 --- a/vision_agent/emb/emb.py +++ /dev/null @@ -1,47 +0,0 @@ -from abc import ABC, abstractmethod -from typing import cast - -import numpy as np -import numpy.typing as npt - - -class Embedder(ABC): - @abstractmethod - def embed(self, text: str) -> npt.NDArray[np.float32]: - pass - - -class SentenceTransformerEmb(Embedder): - def __init__(self, model_name: str = "BAAI/bge-small-en-v1.5"): - from sentence_transformers import SentenceTransformer - - self.model = SentenceTransformer(model_name) - - def embed(self, text: str) -> npt.NDArray[np.float32]: - return cast( - npt.NDArray[np.float32], - self.model.encode([text]).flatten().astype(np.float32), - ) - - -class OpenAIEmb(Embedder): - def __init__(self, model_name: str = "text-embedding-3-small"): - from openai import OpenAI - - self.client = OpenAI() - self.model_name = model_name - - def embed(self, text: str) -> npt.NDArray[np.float32]: - response = self.client.embeddings.create(input=text, model=self.model_name) - return np.array(response.data[0].embedding).astype(np.float32) - - -def get_embedder(name: str) -> Embedder: - if name == "sentence-transformer": - return SentenceTransformerEmb() - elif name == "openai": - return OpenAIEmb() - else: - raise ValueError( - f"Unknown embedder name: {name}, currently support sentence-transformer, openai." - )