✨ feat(slient): allow turn off all outputs
This commit is contained in:
Jianqi Pan
parent
09d331d8f0
commit
117f153b33
1
.gitignore
vendored
1
.gitignore
vendored
@ -2,3 +2,4 @@ dist
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models
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__pycache__
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.pytest_cache
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.ruff_cache
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460
poetry.lock
generated
460
poetry.lock
generated
@ -1,14 +1,60 @@
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@ -110,6 +156,20 @@ files = [
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@ -140,13 +200,13 @@ cron = ["capturer (>=2.4)"]
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@ -154,18 +214,18 @@ test = ["pytest (>=6)"]
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@ -181,13 +241,13 @@ files = [
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@ -220,13 +280,13 @@ tqdm = ["tqdm"]
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@ -340,6 +414,64 @@ docs = ["sphinx"]
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|
||||
@ -899,13 +1099,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.1"
|
||||
version = "2.2.2"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
|
||||
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
|
||||
{file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
|
||||
{file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -917,4 +1117,4 @@ zstd = ["zstandard (>=0.18.0)"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "4ff24b133648154ec552eae7f59f370d80b6506eba1bba7fcd70bd70b8e07af8"
|
||||
content-hash = "0d1dadef7ec968c5b8d1deafa26821c01ae73d42f7198fbd4ed9c7eb40b127c7"
|
||||
|
||||
@ -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"]
|
||||
|
||||
61
stubs/wdtagger/__init__.pyi
Normal file
61
stubs/wdtagger/__init__.pyi
Normal file
@ -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]: ...
|
||||
@ -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)
|
||||
|
||||
@ -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
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user