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["zstandard (>=0.18.0)"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "4ff24b133648154ec552eae7f59f370d80b6506eba1bba7fcd70bd70b8e07af8" +content-hash = "0d1dadef7ec968c5b8d1deafa26821c01ae73d42f7198fbd4ed9c7eb40b127c7" diff --git a/pyproject.toml b/pyproject.toml index 6616725..a7d459e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,22 @@ onnxruntime-gpu = "^1.18.0" [tool.poetry.group.dev.dependencies] pytest = "^8.2.2" pytest-benchmark = "^4.0.0" +ruff = "^0.5.5" +isort = "^5.13.2" +black = "^24.4.2" +mypy = "^1.11.0" + +[tool.isort] +profile = "black" + +[tool.black] +line-length = 120 + +[tool.mypy] +ignore_missing_imports = true + +[tool.poetry.package.include] +"stubs/**/*.pyi" = {} [build-system] requires = ["poetry-core"] diff --git a/stubs/wdtagger/__init__.pyi b/stubs/wdtagger/__init__.pyi new file mode 100644 index 0000000..d7e913a --- /dev/null +++ b/stubs/wdtagger/__init__.pyi @@ -0,0 +1,61 @@ +from pathlib import Path +from typing import Any, Sequence, Union + +import numpy as np +from _typeshed import Incomplete +from PIL import Image + +Input = Union[np.ndarray, Image.Image, str, Path] + +__all__ = ["Tagger"] + +class Result: + general_tag_data: Incomplete + character_tag_data: Incomplete + rating_data: Incomplete + def __init__(self, pred, sep_tags, general_threshold: float = 0.35, character_threshold: float = 0.9) -> None: ... + @property + def general_tags(self): ... + @property + def character_tags(self): ... + @property + def rating(self): ... + @property + def general_tags_string(self) -> str: ... + @property + def character_tags_string(self) -> str: ... + @property + def all_tags(self) -> list[str]: ... + @property + def all_tags_string(self) -> str: ... + +class Tagger: + console: Incomplete + logger: Incomplete + model_target_size: Incomplete + cache_dir: Incomplete + hf_token: Incomplete + def __init__( + self, + model_repo: str = "SmilingWolf/wd-swinv2-tagger-v3", + cache_dir: Incomplete | None = None, + hf_token=..., + loglevel=..., + num_threads: Incomplete | None = None, + providers: Incomplete | None = None, + console: Incomplete | None = None, + ) -> None: ... + sep_tags: Incomplete + model: Incomplete + def load_model( + self, + model_repo, + cache_dir: Incomplete | None = None, + hf_token: Incomplete | None = None, + num_threads: int | None = None, + providers: Sequence[str | tuple[str, dict[Any, Any]]] | None = None, + ): ... + def pil_to_cv2_numpy(self, image): ... + def tag( + self, image: Input | list[Input], general_threshold: float = 0.35, character_threshold: float = 0.9 + ) -> Result | list[Result]: ... diff --git a/tests/benchmark_tagger.py b/tests/benchmark_tagger.py index a66460f..b21ea8a 100644 --- a/tests/benchmark_tagger.py +++ b/tests/benchmark_tagger.py @@ -1,4 +1,5 @@ import os +from typing import Any import pytest from PIL import Image @@ -11,7 +12,7 @@ image_paths = [os.path.join(image_dir, image) for image in os.listdir(image_dir) images = [Image.open(image_path) for image_path in image_paths] -def tag_in_batch(images, batch=1): +def tag_in_batch(images: Any, batch: Any = 1) -> None: for i in range(0, len(images), batch): tagger.tag(images[i : i + batch]) @@ -23,7 +24,7 @@ def tag_in_batch(images, batch=1): disable_gc=True, ) @pytest.mark.parametrize("batch", [1, 2, 4, 8, 16]) -def test_tagger_benchmark(benchmark, batch): +def test_tagger_benchmark(benchmark: Any, batch: Any) -> None: # warmup tag_in_batch(images[:1]) benchmark.pedantic(tag_in_batch, args=(images, batch), iterations=1, rounds=10) diff --git a/wdtagger/__init__.py b/wdtagger/__init__.py index 623fd1b..b0c469c 100644 --- a/wdtagger/__init__.py +++ b/wdtagger/__init__.py @@ -30,7 +30,7 @@ def to_pil(img: Input) -> Image.Image: raise ValueError("Invalid input type.") -def load_labels(dataframe) -> list[str]: +def load_labels(dataframe) -> tuple[Any, list[Any], list[Any], list[Any]]: """Load labels from a dataframe and process tag names. Args: @@ -64,9 +64,7 @@ def load_labels(dataframe) -> list[str]: "|_|", "||_||", ] - name_series = name_series.map( - lambda x: x.replace("_", " ") if x not in kaomojis else x - ) + name_series = name_series.map(lambda x: x.replace("_", " ") if x not in kaomojis else x) tag_names = name_series.tolist() rating_indexes = list(np.where(dataframe["category"] == 9)[0]) general_indexes = list(np.where(dataframe["category"] == 0)[0]) @@ -100,9 +98,7 @@ class Result: # Ratings ratings_names = [labels[i] for i in rating_indexes] rating_data = dict(ratings_names) - rating_data = OrderedDict( - sorted(rating_data.items(), key=lambda x: x[1], reverse=True) - ) + rating_data = OrderedDict(sorted(rating_data.items(), key=lambda x: x[1], reverse=True)) # General tags general_names = [labels[i] for i in general_indexes] @@ -112,9 +108,7 @@ class Result: # Character tags character_names = [labels[i] for i in character_indexes] character_tag = [x for x in character_names if x[1] > character_threshold] - character_tag = OrderedDict( - sorted(character_tag, key=lambda x: x[1], reverse=True) - ) + character_tag = OrderedDict(sorted(character_tag, key=lambda x: x[1], reverse=True)) self.general_tag_data = general_tag self.character_tag_data = character_tag @@ -188,6 +182,7 @@ class Tagger: num_threads=None, providers=None, console=None, + slient=False, ): """Initialize the Tagger object with the model repository and tokens. @@ -200,19 +195,20 @@ class Tagger: providers (list, optional): List of providers for ONNX runtime. Defaults to None. console (rich.console.Console, optional): Rich console object. Defaults to None. """ - - if not console: - from rich import get_console - - self.console = get_console() - else: - self.console = console + self.slient = slient if providers is None: providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] - self.logger = logging.getLogger("wdtagger") - self.logger.setLevel(loglevel) - self.logger.addHandler(RichHandler()) + if not slient: + if not console: + from rich import get_console + + self.console = get_console() + else: + self.console = console + self.logger = logging.getLogger("wdtagger") + self.logger.setLevel(loglevel) + self.logger.addHandler(RichHandler()) self.model_target_size = None self.cache_dir = cache_dir self.hf_token = hf_token @@ -229,8 +225,8 @@ class Tagger: model_repo, cache_dir=None, hf_token=None, - num_threads: int = None, - providers: Sequence[str | tuple[str, dict[Any, Any]]] = None, + num_threads: int | None = None, + providers: Sequence[str | tuple[str, dict[Any, Any]]] | None = None, ): """Load the model and tags from the specified repository. @@ -240,35 +236,41 @@ class Tagger: hf_token (str, optional): HuggingFace token for authentication. Defaults to None. num_threads (int, optional): Number of threads for ONNX runtime. Defaults to None. """ - with self.console.status("Loading model..."): - csv_path = huggingface_hub.hf_hub_download( - model_repo, - LABEL_FILENAME, - cache_dir=cache_dir, - use_auth_token=hf_token, - ) + if not self.slient: + with self.console.status("Loading model..."): + self.do_load_model(model_repo, cache_dir, hf_token, num_threads, providers) + else: + self.do_load_model(model_repo, cache_dir, hf_token, num_threads, providers) - model_path = huggingface_hub.hf_hub_download( - model_repo, - MODEL_FILENAME, - cache_dir=cache_dir, - use_auth_token=hf_token, - ) + def do_load_model(self, model_repo, cache_dir, hf_token, num_threads, providers): + csv_path = huggingface_hub.hf_hub_download( + model_repo, + LABEL_FILENAME, + cache_dir=cache_dir, + use_auth_token=hf_token, + ) - tags_df = pd.read_csv(csv_path) - self.sep_tags = load_labels(tags_df) - options = rt.SessionOptions() - if num_threads: - options.intra_op_num_threads = num_threads - options.inter_op_num_threads = num_threads - model = rt.InferenceSession( - model_path, - options, - providers=providers, - ) - _, height, _, _ = model.get_inputs()[0].shape - self.model_target_size = height - self.model = model + model_path = huggingface_hub.hf_hub_download( + model_repo, + MODEL_FILENAME, + cache_dir=cache_dir, + use_auth_token=hf_token, + ) + + tags_df = pd.read_csv(csv_path) + self.sep_tags = load_labels(tags_df) + options = rt.SessionOptions() + if num_threads: + options.intra_op_num_threads = num_threads + options.inter_op_num_threads = num_threads + model = rt.InferenceSession( + model_path, + options, + providers=providers, + ) + _, height, _, _ = model.get_inputs()[0].shape + self.model_target_size = height + self.model = model def pil_to_cv2_numpy(self, image): """Prepare the image for model input. @@ -330,15 +332,13 @@ class Tagger: input_name = self.model.get_inputs()[0].name label_name = self.model.get_outputs()[0].name preds = self.model.run([label_name], {input_name: image_array})[0] - results = [ - Result(pred, self.sep_tags, general_threshold, character_threshold) - for pred in preds - ] + results = [Result(pred, self.sep_tags, general_threshold, character_threshold) for pred in preds] duration = time.time() - started_at image_length = len(images) - self.logger.info( - f"Tagging {image_length} image{ 's' if image_length > 1 else ''} took {duration:.2f} seconds." - ) + if not self.slient: + self.logger.info( + f"Tagging {image_length} image{ 's' if image_length > 1 else ''} took {duration:.2f} seconds." + ) if input_is_list: return results return results[0] if len(results) == 1 else results