From 25c25cced8df648f998738716533976bc610ce72 Mon Sep 17 00:00:00 2001 From: Matthijs van der Burgh Date: Thu, 27 Jun 2024 13:36:20 +0200 Subject: [PATCH 1/6] Small update --- facenet_pytorch/models/inception_resnet_v1.py | 96 ++++++++++--------- facenet_pytorch/models/mtcnn.py | 5 +- facenet_pytorch/models/utils/download.py | 85 ++++++++-------- .../models/utils/tensorflow2pytorch.py | 12 +-- facenet_pytorch/models/utils/training.py | 49 ++++++---- 5 files changed, 139 insertions(+), 108 deletions(-) diff --git a/facenet_pytorch/models/inception_resnet_v1.py b/facenet_pytorch/models/inception_resnet_v1.py index 35c17232..408fb1f6 100644 --- a/facenet_pytorch/models/inception_resnet_v1.py +++ b/facenet_pytorch/models/inception_resnet_v1.py @@ -1,14 +1,23 @@ +from __future__ import annotations + import os +from pathlib import Path +from typing import TYPE_CHECKING import torch from torch import nn -from torch.nn import functional as F +from torch.nn import functional as tv_functional from .utils.download import download_url_to_file +if TYPE_CHECKING: + from torch.nn.common_types import _size_2_t + class BasicConv2d(nn.Module): - def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): + def __init__( # noqa: PLR0913 + self, in_planes: int, out_planes: int, kernel_size: _size_2_t, stride: _size_2_t, padding: _size_2_t = 0 + ) -> None: super().__init__() self.conv = nn.Conv2d( in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False @@ -21,15 +30,15 @@ def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): ) self.relu = nn.ReLU(inplace=False) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.conv(x) x = self.bn(x) x = self.relu(x) - return x + return x # noqa: RET504 class Block35(nn.Module): - def __init__(self, scale=1.0): + def __init__(self, scale: float = 1.0) -> None: super().__init__() self.scale = scale @@ -49,19 +58,18 @@ def __init__(self, scale=1.0): self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) out = self.conv2d(out) out = out * self.scale + x - out = self.relu(out) - return out + return self.relu(out) class Block17(nn.Module): - def __init__(self, scale=1.0): + def __init__(self, scale: float = 1.0) -> None: super().__init__() self.scale = scale @@ -77,18 +85,17 @@ def __init__(self, scale=1.0): self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x - out = self.relu(out) - return out + return self.relu(out) class Block8(nn.Module): - def __init__(self, scale=1.0, noReLU=False): + def __init__(self, scale: float = 1.0, *, noReLU: bool = False) -> None: # noqa: N803 super().__init__() self.scale = scale @@ -106,7 +113,7 @@ def __init__(self, scale=1.0, noReLU=False): if not self.noReLU: self.relu = nn.ReLU(inplace=False) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) @@ -117,8 +124,8 @@ def forward(self, x): return out -class Mixed_6a(nn.Module): - def __init__(self): +class Mixed6a(nn.Module): + def __init__(self) -> None: super().__init__() self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2) @@ -131,16 +138,15 @@ def __init__(self): self.branch2 = nn.MaxPool2d(3, stride=2) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) - out = torch.cat((x0, x1, x2), 1) - return out + return torch.cat((x0, x1, x2), 1) -class Mixed_7a(nn.Module): - def __init__(self): +class Mixed7a(nn.Module): + def __init__(self) -> None: super().__init__() self.branch0 = nn.Sequential( @@ -159,13 +165,12 @@ def __init__(self): self.branch3 = nn.MaxPool2d(3, stride=2) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) - out = torch.cat((x0, x1, x2, x3), 1) - return out + return torch.cat((x0, x1, x2, x3), 1) class InceptionResnetV1(nn.Module): @@ -187,7 +192,15 @@ class InceptionResnetV1(nn.Module): dropout_prob {float} -- Dropout probability. (default: {0.6}) """ - def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_prob=0.6, device=None): + def __init__( # noqa: PLR0913 + self, + pretrained: str | None = None, + *, + classify: bool = False, + num_classes: int | None = None, + dropout_prob: float = 0.6, + device: torch.device | str | None = None, + ) -> None: super().__init__() # Set simple attributes @@ -200,7 +213,8 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr elif pretrained == "casia-webface": tmp_classes = 10575 elif pretrained is None and self.classify and self.num_classes is None: - raise Exception('If "pretrained" is not specified and "classify" is True, "num_classes" must be specified') + msg = "If 'pretrained' is not specified and 'classify' is True, 'num_classes' must be specified" + raise ValueError(msg) # Define layers self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2) @@ -213,7 +227,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr self.repeat_1 = nn.Sequential( Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17) ) - self.mixed_6a = Mixed_6a() + self.mixed_6a = Mixed6a() self.repeat_2 = nn.Sequential( Block17(scale=0.10), Block17(scale=0.10), @@ -226,7 +240,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr Block17(scale=0.10), Block17(scale=0.10), ) - self.mixed_7a = Mixed_7a() + self.mixed_7a = Mixed7a() self.repeat_3 = nn.Sequential( Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20) ) @@ -248,7 +262,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr self.device = device self.to(device) - def forward(self, x): + def forward(self, x: torch.Tensor) -> torch.Tensor: """Calculate embeddings or logits given a batch of input image tensors. Arguments: @@ -274,10 +288,7 @@ def forward(self, x): x = self.dropout(x) x = self.last_linear(x.view(x.shape[0], -1)) x = self.last_bn(x) - if self.classify: - x = self.logits(x) - else: - x = F.normalize(x, p=2, dim=1) + x = self.logits(x) if self.classify else tv_functional.normalize(x, p=2, dim=1) return x @@ -296,21 +307,20 @@ def load_weights(mdl, name): elif name == "casia-webface": path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt" else: - raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"') + msg = "Pretrained models only exist for 'vggface2' and 'casia-webface'" + raise ValueError(msg) - model_dir = os.path.join(get_torch_home(), "checkpoints") - os.makedirs(model_dir, exist_ok=True) + model_dir = get_torch_home() / "checkpoints" + model_dir.mkdir(parents=True, exist_ok=True) - cached_file = os.path.join(model_dir, os.path.basename(path)) - if not os.path.exists(cached_file): + cached_file = model_dir / Path(path).name + if not cached_file.exists(): download_url_to_file(path, cached_file) state_dict = torch.load(cached_file) mdl.load_state_dict(state_dict) -def get_torch_home(): - torch_home = os.path.expanduser( - os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch")) - ) - return torch_home +def get_torch_home() -> Path: + """Get the torch cache directory.""" + return Path(os.getenv("TORCH_HOME", Path(os.getenv("XDG_CACHE_HOME", "~/.cache")) / "torch")).expanduser() diff --git a/facenet_pytorch/models/mtcnn.py b/facenet_pytorch/models/mtcnn.py index 1c9eae45..7598cb1d 100644 --- a/facenet_pytorch/models/mtcnn.py +++ b/facenet_pytorch/models/mtcnn.py @@ -2,15 +2,18 @@ import importlib.resources import os +from typing import TYPE_CHECKING import numpy as np import torch -from PIL.Image import Image from torch import nn import facenet_pytorch.data from facenet_pytorch.models.utils.detect_face import detect_face, extract_face, get_size +if TYPE_CHECKING: + from PIL.Image import Image + class PNet(nn.Module): """MTCNN PNet. diff --git a/facenet_pytorch/models/utils/download.py b/facenet_pytorch/models/utils/download.py index 6f30331d..418ebba9 100644 --- a/facenet_pytorch/models/utils/download.py +++ b/facenet_pytorch/models/utils/download.py @@ -1,18 +1,22 @@ from __future__ import annotations import hashlib -import os import shutil import tempfile +from pathlib import Path +from typing import TYPE_CHECKING from urllib.request import Request, urlopen +if TYPE_CHECKING: + import _hashlib + try: from tqdm.auto import tqdm # automatically select proper tqdm submodule if available except ImportError: from tqdm import tqdm -def download_url_to_file(url: str, dst: PathLike | str, hash_prefix=None, progress: bool = True) -> None: +def download_url_to_file(url: str, dst: Path | str, hash_prefix: str | None = None, *, progress: bool = True) -> None: """Download object at the given URL to a local path. Args: @@ -20,51 +24,54 @@ def download_url_to_file(url: str, dst: PathLike | str, hash_prefix=None, progre dst (string): Full path where object will be saved, e.g. `/tmp/temporary_file` hash_prefix (string, optional): If not None, the SHA256 downloaded file should start with `hash_prefix`. Default: None - progress (bool, optional): whether or not to display a progress bar to stderr + progress (bool, optional): whether to display a progress bar to stderr Default: True Example: - >>> torch.hub.download_url_to_file('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth', '/tmp/temporary_file') + >>> torch.hub.download_url_to_file("https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", "/tmp/temporary_file") """ file_size = None # We use a different API for python2 since urllib(2) doesn't recognize the CA # certificates in older Python - req = Request(url, headers={"User-Agent": "torch.hub"}) - u = urlopen(req) - meta = u.info() - if hasattr(meta, "getheaders"): - content_length = meta.getheaders("Content-Length") - else: - content_length = meta.get_all("Content-Length") - if content_length is not None and len(content_length) > 0: - file_size = int(content_length[0]) + req = Request(url, headers={"User-Agent": "torch.hub"}) # noqa: S310 + with urlopen(req) as response: # noqa: S310 + meta = response.info() + if hasattr(meta, "getheaders"): + content_length = meta.getheaders("Content-Length") + else: + content_length = meta.get_all("Content-Length") + if content_length is not None and len(content_length) > 0: + file_size = int(content_length[0]) - # We deliberately save it in a temp file and move it after - # download is complete. This prevents a local working checkpoint - # being overridden by a broken download. - dst = os.path.expanduser(dst) - dst_dir = os.path.dirname(dst) - f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir) + # We deliberately save it in a temp file and move it after + # download is complete. This prevents a local working checkpoint + # being overridden by a broken download. + dst = dst.expanduser() + dst_dir = dst.parent + f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir) - try: + sha256: _hashlib.HASH | None = None if hash_prefix is not None: sha256 = hashlib.sha256() - with tqdm(total=file_size, disable=not progress, unit="B", unit_scale=True, unit_divisor=1024) as pbar: - while True: - buffer = u.read(8192) - if len(buffer) == 0: - break - f.write(buffer) - if hash_prefix is not None: - sha256.update(buffer) - pbar.update(len(buffer)) - f.close() - if hash_prefix is not None: - digest = sha256.hexdigest() - if digest[: len(hash_prefix)] != hash_prefix: - raise RuntimeError(f'invalid hash value (expected "{hash_prefix}", got "{digest}")') - shutil.move(f.name, dst) - finally: - f.close() - if os.path.exists(f.name): - os.remove(f.name) + try: + with tqdm(total=file_size, disable=not progress, unit="B", unit_scale=True, unit_divisor=1024) as pbar: + while True: + buffer = response.read(8192) + if len(buffer) == 0: + break + f.write(buffer) + if sha256 is not None: + sha256.update(buffer) + pbar.update(len(buffer)) + + f.close() + if sha256 is not None: + digest = sha256.hexdigest() + if digest[: len(hash_prefix)] != hash_prefix: + msg = f"invalid hash value (expected '{hash_prefix}', got '{digest}')" + raise RuntimeError(msg) + shutil.move(f.name, dst) + finally: + f.close() + if Path(f.name).exists(): + Path(f.name).unlink() diff --git a/facenet_pytorch/models/utils/tensorflow2pytorch.py b/facenet_pytorch/models/utils/tensorflow2pytorch.py index 51aa4196..c8bf5eeb 100644 --- a/facenet_pytorch/models/utils/tensorflow2pytorch.py +++ b/facenet_pytorch/models/utils/tensorflow2pytorch.py @@ -31,8 +31,8 @@ def import_tf_params(tf_mdl_dir, sess): tf_layers = tf.trainable_variables() tf_params = sess.run(tf_layers) - tf_shapes = [p.shape for p in tf_params] - tf_layers = [l.name for l in tf_layers] + tf_shapes = [param.shape for param in tf_params] + tf_layers = [layer.name for layer in tf_layers] if not callable(tf_mdl_dir): path = os.path.join(tf_mdl_dir, "layer_description.json") @@ -232,11 +232,11 @@ def compare_model_outputs(pt_mdl, sess, test_data): def compare_mtcnn(pt_mdl, tf_fun, sess, ind, test_data): - tf_mdls = tf_fun(sess) - tf_mdl = tf_mdls[ind] + tf_modules = tf_fun(sess) + tf_module = tf_modules[ind] print("\nPassing test data through TF model\n") - tf_output = tf_mdl(test_data.numpy()) + tf_output = tf_module(test_data.numpy()) tf_output = [torch.tensor(out) for out in tf_output] print("\n".join([str(o.view(-1)[:10]) for o in tf_output])) @@ -368,7 +368,7 @@ def tensorflow2pytorch(): } print("\nLoad PNet weights and save\n") - tf_mdl_dir = lambda sess: detect_face.create_mtcnn(sess, None) + tf_mdl_dir = lambda session: detect_face.create_mtcnn(session, None) mdl = PNet() data_name = "pnet" load_tf_model_weights(mdl, lookup_pnet, tf_mdl_dir, is_resnet=False, arg_num=0) diff --git a/facenet_pytorch/models/utils/training.py b/facenet_pytorch/models/utils/training.py index 90090c81..a0761f2f 100644 --- a/facenet_pytorch/models/utils/training.py +++ b/facenet_pytorch/models/utils/training.py @@ -1,7 +1,14 @@ +from __future__ import annotations + import time +from typing import TYPE_CHECKING, Callable import torch +if TYPE_CHECKING: + import torch.utils.data + import torch.utils.tensorboard + class Logger: def __init__(self, mode, length, calculate_mean=False): @@ -11,7 +18,7 @@ def __init__(self, mode, length, calculate_mean=False): if self.calculate_mean: self.fn = lambda x, i: x / (i + 1) else: - self.fn = lambda x, i: x + self.fn = lambda x, _: x def __call__(self, loss, metrics, i): track_str = f"\r{self.mode} | {i + 1:5d}/{self.length:<5d}| " @@ -24,6 +31,7 @@ def __call__(self, loss, metrics, i): class BatchTimer: """Batch timing class. + Use this class for tracking training and testing time/rate per batch or per sample. Keyword Arguments: @@ -33,13 +41,13 @@ class BatchTimer: (default: {True}) """ - def __init__(self, rate=True, per_sample=True): + def __init__(self, *, rate: bool = True, per_sample: bool = True): self.start = time.time() self.end = None - self.rate = rate - self.per_sample = per_sample + self.rate: bool = rate + self.per_sample: bool = per_sample - def __call__(self, y_pred, y): + def __call__(self, y_pred, y) -> torch.Tensor: self.end = time.time() elapsed = self.end - self.start self.start = self.end @@ -59,38 +67,41 @@ def accuracy(logits, y): def pass_epoch( - model, - loss_fn, - loader, - optimizer=None, - scheduler=None, - batch_metrics={"time": BatchTimer()}, - show_running=True, - device="cpu", - writer=None, -): + model: torch.nn.Module, + loss_fn: Callable, + loader: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer | None = None, + scheduler: torch.optim.lr_scheduler.LRScheduler = None, + batch_metrics: dict | None = None, + writer: torch.utils.tensorboard.SummaryWriter = None, + device: torch.device | str = "cpu", + *, + show_running: bool = True, +) -> tuple[torch.Tensor, dict]: """Train or evaluate over a data epoch. Arguments: model {torch.nn.Module} -- Pytorch model. loss_fn {callable} -- A function to compute (scalar) loss. loader {torch.utils.data.DataLoader} -- A pytorch data loader. - - Keyword Arguments: optimizer {torch.optim.Optimizer} -- A pytorch optimizer. scheduler {torch.optim.lr_scheduler._LRScheduler} -- LR scheduler (default: {None}) batch_metrics {dict} -- Dictionary of metric functions to call on each batch. The default is a simple timer. A progressive average of these metrics, along with the average loss, is printed every batch. (default: {{'time': iter_timer()}}) + writer {torch.utils.tensorboard.SummaryWriter} -- Tensorboard SummaryWriter. (default: {None}) + + Keyword Arguments: + device {str or torch.device} -- Device for pytorch to use. (default: {'cpu'}) show_running {bool} -- Whether or not to print losses and metrics for the current batch or rolling averages. (default: {False}) - device {str or torch.device} -- Device for pytorch to use. (default: {'cpu'}) - writer {torch.utils.tensorboard.SummaryWriter} -- Tensorboard SummaryWriter. (default: {None}) Returns: tuple(torch.Tensor, dict) -- A tuple of the average loss and a dictionary of average metric values across the epoch. """ + if batch_metrics is None: + batch_metrics = {"time": BatchTimer()} mode = "Train" if model.training else "Valid" logger = Logger(mode, length=len(loader), calculate_mean=show_running) loss = 0 From 5367a4883d6e9c9a1f5afc47fcad2355483f70f4 Mon Sep 17 00:00:00 2001 From: Matthijs van der Burgh Date: Fri, 28 Jun 2024 08:50:47 +0200 Subject: [PATCH 2/6] (CI) fix typo --- .github/workflows/python.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml index d3b9360d..47342f87 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/python.yml @@ -21,7 +21,7 @@ jobs: - name: Install Poetry uses: snok/install-poetry@v1 with: - virtualenv-create: true + virtualenvs-create: true virtualenvs-in-project: true - name: Poetry cache id: poetry_cache From d15b341efad9e4c19cebe5764c25385a5b53a2e6 Mon Sep 17 00:00:00 2001 From: Matthijs van der Burgh Date: Fri, 28 Jun 2024 10:42:26 +0200 Subject: [PATCH 3/6] Dependency update --- poetry.lock | 320 ++++++++++++++++++++++++++-------------------------- 1 file changed, 160 insertions(+), 160 deletions(-) diff --git a/poetry.lock b/poetry.lock index b72466a0..b287cdd8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -574,63 +574,63 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"] [[package]] name = "coverage" -version = "7.5.3" +version = "7.5.4" description = "Code coverage measurement for Python" optional = false python-versions = ">=3.8" files = [ - {file = "coverage-7.5.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a6519d917abb15e12380406d721e37613e2a67d166f9fb7e5a8ce0375744cd45"}, - {file = "coverage-7.5.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:aea7da970f1feccf48be7335f8b2ca64baf9b589d79e05b9397a06696ce1a1ec"}, - {file = "coverage-7.5.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:923b7b1c717bd0f0f92d862d1ff51d9b2b55dbbd133e05680204465f454bb286"}, - {file = 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[tool.ruff] force-exclude = true line-length = 120 From 76ba85d540315d9fb39957c63f6f4ae369351d22 Mon Sep 17 00:00:00 2001 From: Matthijs van der Burgh Date: Fri, 28 Jun 2024 10:58:50 +0200 Subject: [PATCH 5/6] Check hash --- facenet_pytorch/models/inception_resnet_v1.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/facenet_pytorch/models/inception_resnet_v1.py b/facenet_pytorch/models/inception_resnet_v1.py index 408fb1f6..db13c9c1 100644 --- a/facenet_pytorch/models/inception_resnet_v1.py +++ b/facenet_pytorch/models/inception_resnet_v1.py @@ -292,7 +292,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x -def load_weights(mdl, name): +def load_weights(mdl: torch.nn.Module, name: str) -> None: """Download pretrained state_dict and load into model. Arguments: @@ -304,8 +304,10 @@ def load_weights(mdl, name): """ if name == "vggface2": path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt" + hash_prefix = "281cebca8662831adb987a874bdcb36e73f5b1c6dc5ee5878f305e985625d99b" elif name == "casia-webface": path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt" + hash_prefix = "7a67afdbbc995fce5e10128675e318799a70698c2f433ba75dd7eb9a2f096e7d" else: msg = "Pretrained models only exist for 'vggface2' and 'casia-webface'" raise ValueError(msg) @@ -315,7 +317,7 @@ def load_weights(mdl, name): cached_file = model_dir / Path(path).name if not cached_file.exists(): - download_url_to_file(path, cached_file) + download_url_to_file(path, cached_file, hash_prefix) state_dict = torch.load(cached_file) mdl.load_state_dict(state_dict) From e13f7878ceacae0b4d625aa4970e091c3fec423e Mon Sep 17 00:00:00 2001 From: Matthijs van der Burgh Date: Fri, 28 Jun 2024 11:08:35 +0200 Subject: [PATCH 6/6] Coverage ignore ImportError --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 3eb2a9cd..432124a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -78,6 +78,7 @@ exclude_also = [ "if TYPE_CHECKING:", "class .*\\bProtocol\\):", "@(abc\\.)?abstractmethod", + "except ImportError:", ] [tool.ruff]