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j%!}gugd-%hO#gqs{&UhD<`4PT3nd{Xp+E2c-|zijakUO9 literal 0 HcmV?d00001 diff --git a/facenet_pytorch/models/inception_resnet_v1.py b/facenet_pytorch/models/inception_resnet_v1.py index d167ac90..d3a2a283 100644 --- a/facenet_pytorch/models/inception_resnet_v1.py +++ b/facenet_pytorch/models/inception_resnet_v1.py @@ -1,8 +1,6 @@ import os -import requests import torch -from requests.adapters import HTTPAdapter from torch import nn from torch.nn import functional as F diff --git a/facenet_pytorch/models/mtcnn.py b/facenet_pytorch/models/mtcnn.py index 202943d0..31589f70 100644 --- a/facenet_pytorch/models/mtcnn.py +++ b/facenet_pytorch/models/mtcnn.py @@ -1,34 +1,64 @@ -import os +from __future__ import annotations + import importlib.resources +import os +from collections import OrderedDict import numpy as np import torch +from PIL.Image import Image from torch import nn -from facenet_pytorch.models.utils.detect_face import detect_face, extract_face import facenet_pytorch.data +from facenet_pytorch.models.utils.detect_face import detect_face, extract_face + + +class Flatten(nn.Module): + def __init__(self): + super().__init__(training=False) + + @staticmethod + def forward(x): + """Flatten. + + Arguments: + x: a float tensor with shape [batch_size, c, h, w]. + + Returns: + a float tensor with shape [batch_size, c*h*w]. + """ + # without this pretrained model isn't working + x = x.transpose(3, 2).contiguous() + + return x.view(x.size(0), -1) class PNet(nn.Module): """MTCNN PNet. Keyword Arguments: - pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True}) + pretrained {bool} -- Whether to load saved pretrained weights (default: {True}) """ - def __init__(self, pretrained=True): + def __init__(self, pretrained=True) -> None: super().__init__() - self.conv1 = nn.Conv2d(3, 10, kernel_size=3) - self.prelu1 = nn.PReLU(10) - self.pool1 = nn.MaxPool2d(2, 2, ceil_mode=True) - self.conv2 = nn.Conv2d(10, 16, kernel_size=3) - self.prelu2 = nn.PReLU(16) - self.conv3 = nn.Conv2d(16, 32, kernel_size=3) - self.prelu3 = nn.PReLU(32) - self.conv4_1 = nn.Conv2d(32, 2, kernel_size=1) - self.softmax4_1 = nn.Softmax(dim=1) - self.conv4_2 = nn.Conv2d(32, 4, kernel_size=1) + self.features = nn.Sequential( + OrderedDict( + [ + ("conv1", nn.Conv2d(3, 10, 3, 1)), + ("prelu1", nn.PReLU(10)), + ("pool1", nn.MaxPool2d(2, 2, ceil_mode=True)), + ("conv2", nn.Conv2d(10, 16, 3, 1)), + ("prelu2", nn.PReLU(16)), + ("conv3", nn.Conv2d(16, 32, 3, 1)), + ("prelu3", nn.PReLU(32)), + ] + ) + ) + + self.conv4_1 = nn.Conv2d(32, 2, 1, 1) + self.conv4_2 = nn.Conv2d(32, 4, 1, 1) self.training = False @@ -37,17 +67,20 @@ def __init__(self, pretrained=True): state_dict = torch.load(state_dict_path) self.load_state_dict(state_dict) - def forward(self, x): - x = self.conv1(x) - x = self.prelu1(x) - x = self.pool1(x) - x = self.conv2(x) - x = self.prelu2(x) - x = self.conv3(x) - x = self.prelu3(x) + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Forward pass of PNet. + + Arguments: + x: a float tensor with shape [batch_size, 3, h, w]. + + Returns: + b: a float tensor with shape [batch_size, 4, h', w']. + a: a float tensor with shape [batch_size, 2, h', w']. + """ + x = self.features(x) a = self.conv4_1(x) - a = self.softmax4_1(a) b = self.conv4_2(x) + a = nn.functional.softmax(a, dim=1) return b, a @@ -55,7 +88,7 @@ class RNet(nn.Module): """MTCNN RNet. Keyword Arguments: - pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True}) + pretrained {bool} -- Whether to load saved pretrained weights (default: {True}) """ def __init__(self, pretrained=True): @@ -82,7 +115,7 @@ def __init__(self, pretrained=True): state_dict = torch.load(state_dict_path) self.load_state_dict(state_dict) - def forward(self, x): + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: x = self.conv1(x) x = self.prelu1(x) x = self.pool1(x) @@ -107,7 +140,7 @@ class ONet(nn.Module): pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True}) """ - def __init__(self, pretrained=True): + def __init__(self, pretrained: bool = True): super().__init__() self.conv1 = nn.Conv2d(3, 32, kernel_size=3) @@ -135,7 +168,7 @@ def __init__(self, pretrained=True): state_dict = torch.load(state_dict_path) self.load_state_dict(state_dict) - def forward(self, x): + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: x = self.conv1(x) x = self.prelu1(x) x = self.pool1(x) @@ -198,27 +231,27 @@ class MTCNN(nn.Module): """ def __init__( - self, - image_size=160, - margin=0, - min_face_size=20, - thresholds=[0.6, 0.7, 0.7], - factor=0.709, - post_process=True, - select_largest=True, - selection_method=None, - keep_all=False, - device=None, + self, + image_size: int = 160, + margin: int = 0, + min_face_size: int = 20, + thresholds=None, + factor: float = 0.709, + post_process: bool = True, + selection_method: str = "largest", + keep_all: bool = False, + device: torch.device | str | None = None, ): super().__init__() + if thresholds is None: + thresholds = [0.6, 0.7, 0.7] self.image_size = image_size self.margin = margin self.min_face_size = min_face_size self.thresholds = thresholds self.factor = factor self.post_process = post_process - self.select_largest = select_largest self.keep_all = keep_all self.selection_method = selection_method @@ -231,11 +264,10 @@ def __init__( self.device = device self.to(device) - if not self.selection_method: - self.selection_method = "largest" if self.select_largest else "probability" + def forward(self, img, save_path=None): + """Run MTCNN face detection on a PIL image or numpy array. - def forward(self, img, save_path=None, return_prob=False): - """Run MTCNN face detection on a PIL image or numpy array. This method performs both + This method performs both detection and extraction of faces, returning tensors representing detected faces rather than the bounding boxes. To access bounding boxes, see the MTCNN.detect() method below. @@ -248,12 +280,10 @@ def forward(self, img, save_path=None, return_prob=False): face image is not, so it is a true representation of the face in the input image. If `img` is a list of images, `save_path` should be a list of equal length. (default: {None}) - return_prob {bool} -- Whether or not to return the detection probability. - (default: {False}) Returns: Union[torch.Tensor, tuple(torch.tensor, float)] -- If detected, cropped image of a face - with dimensions 3 x image_size x image_size. Optionally, the probability that a + with dimensions 3 x image_size x image_size. Additionally, the probability that a face was detected. If self.keep_all is True, n detected faces are returned in an n x 3 x image_size x image_size tensor with an optional list of detection probabilities. If `img` is a list of images, the item(s) returned have an extra @@ -262,29 +292,21 @@ def forward(self, img, save_path=None, return_prob=False): Example: >>> from facenet_pytorch import MTCNN >>> mtcnn = MTCNN() - >>> face_tensor, prob = mtcnn(img, save_path='face.png', return_prob=True) + >>> face_tensor, prob = mtcnn(img, save_path='face.png') """ - # Detect faces - batch_boxes, batch_probs, batch_points = self.detect(img, landmarks=True) + batch_boxes, batch_probs, batch_points = self.detect(img) # Select faces if not self.keep_all: batch_boxes, batch_probs, batch_points = self.select_boxes( - batch_boxes, - batch_probs, - batch_points, - img, - method=self.selection_method, + batch_boxes, batch_probs, batch_points, img, method=self.selection_method ) # Extract faces faces = self.extract(img, batch_boxes, save_path) - if return_prob: - return faces, batch_probs - else: - return faces + return faces, batch_probs - def detect(self, img, landmarks=False): + def detect(self, img: Image | np.ndarray | torch.Tensor | list) -> list[tuple[np.ndarray, np.ndarray, np.ndarray]]: """Detect all faces in PIL image and return bounding boxes and optional facial landmarks. This method is used by the forward method and is also useful for face detection tasks @@ -295,10 +317,6 @@ def detect(self, img, landmarks=False): Arguments: img {PIL.Image, np.ndarray, or list} -- A PIL image, np.ndarray, torch.Tensor, or list. - Keyword Arguments: - landmarks {bool} -- Whether to return facial landmarks in addition to bounding boxes. - (default: {False}) - Returns: tuple(numpy.ndarray, list) -- For N detected faces, a tuple containing an Nx4 array of bounding boxes and a length N list of detection probabilities. @@ -323,34 +341,27 @@ def detect(self, img, landmarks=False): ... extract_face(img, box, save_path='detected_face_{}.png'.format(i)) >>> img_draw.save('annotated_faces.png') """ - with torch.no_grad(): batch_boxes, batch_points = detect_face( - img, - self.min_face_size, - self.pnet, - self.rnet, - self.onet, - self.thresholds, - self.factor, - self.device, + img, self.min_face_size, self.pnet, self.rnet, self.onet, self.thresholds, self.factor, self.device ) boxes, probs, points = [], [], [] for box, point in zip(batch_boxes, batch_points): - box = np.array(box) - point = np.array(point) + # box = np.array(box) + # point = np.array(point) if len(box) == 0: - boxes.append(None) - probs.append([None]) - points.append(None) - elif self.select_largest: - box_order = np.argsort((box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1]))[::-1] - box = box[box_order] - point = point[box_order] - boxes.append(box[:, :4]) - probs.append(box[:, 4]) - points.append(point) + pass + # boxes.append(None) + # probs.append([None]) + # points.append(None) + # elif self.select_largest: + # box_order = np.argsort((box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1]))[::-1] + # box = box[box_order] + # point = point[box_order] + # boxes.append(box[:, :4]) + # probs.append(box[:, 4]) + # points.append(point) else: boxes.append(box[:, :4]) probs.append(box[:, 4]) @@ -359,29 +370,19 @@ def detect(self, img, landmarks=False): probs = np.array(probs, dtype=object) points = np.array(points, dtype=object) - if ( - not isinstance(img, (list, tuple)) - and not (isinstance(img, np.ndarray) and len(img.shape) == 4) - and not (isinstance(img, torch.Tensor) and len(img.shape) == 4) - ): - boxes = boxes[0] - probs = probs[0] - points = points[0] - - if landmarks: - return boxes, probs, points + # if ( + # not isinstance(img, (list, tuple)) + # and not (isinstance(img, np.ndarray) and len(img.shape) == 4) + # and not (isinstance(img, torch.Tensor) and len(img.shape) == 4) + # ): + # boxes = boxes[0] + # probs = probs[0] + # points = points[0] - return boxes, probs + return boxes, probs, points def select_boxes( - self, - all_boxes, - all_probs, - all_points, - imgs, - method="probability", - threshold=0.9, - center_weight=2.0, + self, all_boxes, all_probs, all_points, imgs, method="probability", threshold=0.9, center_weight=2.0 ): """Selects a single box from multiple for a given image using one of multiple heuristics. @@ -409,13 +410,12 @@ def select_boxes( tuple(numpy.ndarray, numpy.ndarray, numpy.ndarray) -- nx4 ndarray of bounding boxes for n images. Ix0 array of probabilities for each box, array of landmark points. """ - # copying batch detection from extract, but would be easier to ensure detect creates consistent output. batch_mode = True if ( - not isinstance(imgs, (list, tuple)) - and not (isinstance(imgs, np.ndarray) and len(imgs.shape) == 4) - and not (isinstance(imgs, torch.Tensor) and len(imgs.shape) == 4) + not isinstance(imgs, (list, tuple)) + and not (isinstance(imgs, np.ndarray) and len(imgs.shape) == 4) + and not (isinstance(imgs, torch.Tensor) and len(imgs.shape) == 4) ): imgs = [imgs] all_boxes = [all_boxes] @@ -425,7 +425,6 @@ def select_boxes( selected_boxes, selected_probs, selected_points = [], [], [] for boxes, points, probs, img in zip(all_boxes, all_points, all_probs, imgs): - if boxes is None: selected_boxes.append(None) selected_probs.append([None]) @@ -444,14 +443,7 @@ def select_boxes( elif method == "center_weighted_size": box_sizes = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) img_center = (img.width / 2, img.height / 2) - box_centers = np.array( - list( - zip( - (boxes[:, 0] + boxes[:, 2]) / 2, - (boxes[:, 1] + boxes[:, 3]) / 2, - ) - ) - ) + box_centers = np.array(list(zip((boxes[:, 0] + boxes[:, 2]) / 2, (boxes[:, 1] + boxes[:, 3]) / 2))) offsets = box_centers - img_center offset_dist_squared = np.sum(np.power(offsets, 2.0), 1) box_order = np.argsort(box_sizes - offset_dist_squared * center_weight)[::-1] @@ -487,9 +479,9 @@ def extract(self, img, batch_boxes, save_path): # Determine if a batch or single image was passed batch_mode = True if ( - not isinstance(img, (list, tuple)) - and not (isinstance(img, np.ndarray) and len(img.shape) == 4) - and not (isinstance(img, torch.Tensor) and len(img.shape) == 4) + not isinstance(img, (list, tuple)) + and not (isinstance(img, np.ndarray) and len(img.shape) == 4) + and not (isinstance(img, torch.Tensor) and len(img.shape) == 4) ): img = [img] batch_boxes = [batch_boxes] diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..96148b40 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,159 @@ +[tool.ruff] +force-exclude = true +line-length = 120 +output-format = "full" +target-version = "py38" # Should match the minumum version in pyproject.toml +exclude = [ + ".git", + ".svn", + ".mypy_cache", + ".mypy", + ".pytest_cache", + ".ruff_cache", + "__pycache__", + ".coverage", + "coverage", + ".tox", + ".idea", + ".venv", + "node_modules", + "test_*.py", + "tests_*.py", +] + +[tool.ruff.lint] +select = [ + # Pyflakes + "F", + # PYcodestyle + "E", # Errors + "W", # Warnings + # mccabe + # "C90", + # isort + "I", + # pep8-naming + "N", + # pydocstyle + "D", + # pyupgrade + "UP", + # flake8 + "YTT", # flake8-2020 + "ANN", # flake8-annotations + "ASYNC", # flake8-async + "S", # flake8-bandit + "BLE", # flake8-blind-except + "FBT", #flake8-boolean-trap + "B", # flake8-bugbear + "A", # flake8-builtins + "COM", # flake8-commas + # "CPY", # flake8-copyright + "C4", # flake8-comprehensions + "DTZ", # flake8-datetimez + "T10", # flake8-debugger + # "DJ", # flake8-django + "EM", # flake8-errmsg + "EXE", # flake8-executable + "FA", # flake8-future-annotations + "ISC", # flake8-implicit-str-concat + "ICN", # flake8-import-conventions + "LOG", # flake8-logging + "G", # flake8-logging-format + "INP", # flake8-no-pep420 + "PIE", # flake8-pie + "T20", # flake8-print + "PYI", # flake8-pyi + "PT", # flake8-pytest-style + "Q", # flake8-quotes + "RSE", # flake8-raise + "RET", # flake8-return + "SLF", # flake8-self + "SLOT", # flake8-slots + "SIM", # flake8-simplify + "TID", # flake8-tidy-imports + "TCH", # flake8-type-checking + "INT", # flake8-gettext + "ARG", # flake8-unsued-arguments + "PTH", # flake8-use-pathlib + "TD", # flake8-todos + # "FIX", # flake8-fixme + # eradicate (remove commented code) + # "ERA", + # pandas-vet + "PD", + # pygrep-hooks + "PGH", + # Pylint + "PL", + # "PLC", # Convention + # "PLE", # Error + # "PLR", # Refactor + # "PLW", # Warning + # tryceratrops (exception handling linting) + "TRY", + # flynt + "FLY", + # NumPy-specific rules + "NPY", + # Airflow + # "AIR", + # Perflint + "PERF", + # refurb + # "FURB", # in preview + # Ruff-specific rules + "RUF", +] +# Ignore codes such as "F401" +ignore = [ + "ANN002", # Don't annotate *args + "ANN003", # Don't annotate **kwargs + "ANN101", # Don't annotate self + "ANN102", # Don't annotate cls + "COM812", # Possibly conflicting with the formatter; Missing trailing comma + "D100", # Missing docstring in public module + "D104", # Missing docstring in public package + "D206", # Fixed by the formatter; Docstring should be indented with spaces, not tabs + "D300", # Fixed by the formatter; Use """triple double quotes""" (found '''triple single quotes''') + "E111", # Fixed by the formatter; Indentation is not a multiple of four + "E114", # Fixed by the formatter; Indentation is not a multiple of four (comment) + "E117", # Fixed by the formatter; Over-indented + "ISC001", # Possibly conflicting with the formatter; Avoid implicit string concatenation + "Q000", # Fixed by the formatter; Fix bad inline quotes + "Q001", # Fixed by the formatter; Fix bad multiline quotes + "Q002", # Fixed by the formatter; Fix bad docstring quotes + "Q003", # Fixed by the formatter; Fix avoidable escape quotes + "RET501", # Allow explicit return of None, even when None is the only optional return value + "TD003", # No need add a link following a TODO + "W191", # Fixed by the formatter; Indentation contains tabs +] + +[tool.ruff.lint.flake8-annotations] +allow-star-arg-any = true +mypy-init-return = true +suppress-dummy-args = true + +[tool.ruff.lint.flake8-quotes] +docstring-quotes = "double" +inline-quotes = "double" +multiline-quotes = "double" + +[tool.ruff.lint.flake8-type-checking] +quote-annotations = false + +[tool.ruff.lint.isort] +case-sensitive = true +combine-as-imports = true +split-on-trailing-comma = false + +[tool.ruff.lint.pep8-naming] +ignore-names = ["i", "j", "k", "ex", "_", "pk", "x", "y", "z", "e", "x1", "x2", "y1", "y2", "w", "h", "cx", "cy", "iou", "k", "v", "f", "ft"] + +[tool.ruff.lint.pydocstyle] +convention = "google" + +[tool.ruff.format] +indent-style = "space" +quote-style = "double" +skip-magic-trailing-comma = true diff --git a/tests/perf_test.py b/tests/perf_test.py index 2b2eb39b..70b5d756 100644 --- a/tests/perf_test.py +++ b/tests/perf_test.py @@ -1,6 +1,7 @@ import time import torch +import facenet_pytorch from facenet_pytorch import MTCNN, training from torch.utils.data import DataLoader, RandomSampler from torchvision import datasets, transforms @@ -11,6 +12,7 @@ def main(): device = "cuda" if torch.cuda.is_available() else "cpu" print(f'Running on device "{device}"') + mtcnn = MTCNN(device=device) batch_size = 32

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