Merge branch 'main' into model_yaml_args

docs/reference/yolo/utils/checks.md

@@ -43,6 +43,11 @@ keywords: YOLO, Ultralytics, Utils, Checks, image sizing, version updates, font
 ### ::: ultralytics.yolo.utils.checks.check_requirements
 <br><br>
 
+## check_torchvision
+---
+### ::: ultralytics.yolo.utils.checks.check_torchvision
+<br><br>
+
 ## check_suffix
 ---
 ### ::: ultralytics.yolo.utils.checks.check_suffix

examples/tutorial.ipynb

@@ -66,7 +66,7 @@
         "import ultralytics\n",
         "ultralytics.checks()"
       ],
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -102,7 +102,7 @@
         "# Run inference on an image with YOLOv8n\n",
         "!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
       ],
-      "execution_count": 2,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -169,7 +169,7 @@
         "# Validate YOLOv8n on COCO128 val\n",
         "!yolo val model=yolov8n.pt data=coco128.yaml"
       ],
-      "execution_count": 3,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -293,7 +293,7 @@
         "# Train YOLOv8n on COCO128 for 3 epochs\n",
         "!yolo train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
       ],
-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -454,8 +454,8 @@
         "- 💡 ProTip: Export to [TensorRT](https://developer.nvidia.com/tensorrt) for up to 5x GPU speedup.\n",
         "\n",
         "\n",
-        "| Format                                                                     | `format=`          | Model                     |\n",
-        "|----------------------------------------------------------------------------|--------------------|---------------------------|\n",
+        "| Format                                                                     | `format` Argument | Model                     |\n",
+        "|----------------------------------------------------------------------------|-------------------|---------------------------|\n",
         "| [PyTorch](https://pytorch.org/)                                            | -                 | `yolov8n.pt`              |\n",
         "| [TorchScript](https://pytorch.org/docs/stable/jit.html)                    | `torchscript`     | `yolov8n.torchscript`     |\n",
         "| [ONNX](https://onnx.ai/)                                                   | `onnx`            | `yolov8n.onnx`            |\n",
@@ -468,7 +468,7 @@
         "| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  |\n",
         "| [TensorFlow.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      |\n",
         "| [PaddlePaddle](https://github.com/PaddlePaddle)                            | `paddle`          | `yolov8n_paddle_model/`   |\n",
-        "\n"
+        "| [NCNN](https://github.com/Tencent/ncnn)                                    | `ncnn`            | `yolov8n_ncnn_model/`     |\n"
       ],
       "metadata": {
         "id": "nPZZeNrLCQG6"
@@ -486,7 +486,7 @@
         "id": "CYIjW4igCjqD",
         "outputId": "fc41bf7a-0ea2-41a6-9ec5-dd0455af43bc"
       },
-      "execution_count": 5,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -533,7 +533,7 @@
         "results = model.train(data='coco128.yaml', epochs=3)  # train the model\n",
         "results = model.val()  # evaluate model performance on the validation set\n",
         "results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image\n",
-        "success = model.export(format='onnx')  # export the model to ONNX format"
+        "results = model.export(format='onnx')  # export the model to ONNX format"
       ],
       "metadata": {
         "id": "bpF9-vS_DAaf"
@@ -677,9 +677,8 @@
       "cell_type": "code",
       "source": [
         "# Git clone and run tests on updates branch\n",
-        "!git clone https://github.com/ultralytics/ultralytics -b updates\n",
-        "%pip install -qe ultralytics\n",
-        "!pytest ultralytics/tests"
+        "!git clone https://github.com/ultralytics/ultralytics -b main\n",
+        "%pip install -qe ultralytics"
       ],
       "metadata": {
         "id": "uRKlwxSJdhd1"
@@ -687,6 +686,18 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run tests (Git clone only)\n",
+        "!pytest ultralytics/tests"
+      ],
+      "metadata": {
+        "id": "GtPlh7mcCGZX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.132'
+__version__ = '8.0.133'
 
 from ultralytics.hub import start
 from ultralytics.vit.rtdetr import RTDETR

ultralytics/hub/session.py

@@ -25,11 +25,11 @@ class HUBTrainingSession:
         model_id (str): Identifier for the YOLOv5 model being trained.
         model_url (str): URL for the model in Ultralytics HUB.
         api_url (str): API URL for the model in Ultralytics HUB.
-        auth_header (Dict): Authentication header for the Ultralytics HUB API requests.
-        rate_limits (Dict): Rate limits for different API calls (in seconds).
-        timers (Dict): Timers for rate limiting.
-        metrics_queue (Dict): Queue for the model's metrics.
-        model (Dict): Model data fetched from Ultralytics HUB.
+        auth_header (dict): Authentication header for the Ultralytics HUB API requests.
+        rate_limits (dict): Rate limits for different API calls (in seconds).
+        timers (dict): Timers for rate limiting.
+        metrics_queue (dict): Queue for the model's metrics.
+        model (dict): Model data fetched from Ultralytics HUB.
         alive (bool): Indicates if the heartbeat loop is active.
     """
 

ultralytics/nn/autobackend.py

@@ -137,17 +137,17 @@ class AutoBackend(nn.Module):
             LOGGER.info(f'Loading {w} for OpenVINO inference...')
             check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
             from openvino.runtime import Core, Layout, get_batch  # noqa
-            ie = Core()
+            core = Core()
             w = Path(w)
             if not w.is_file():  # if not *.xml
                 w = next(w.glob('*.xml'))  # get *.xml file from *_openvino_model dir
-            network = ie.read_model(model=str(w), weights=w.with_suffix('.bin'))
-            if network.get_parameters()[0].get_layout().empty:
-                network.get_parameters()[0].set_layout(Layout('NCHW'))
-            batch_dim = get_batch(network)
+            ov_model = core.read_model(model=str(w), weights=w.with_suffix('.bin'))
+            if ov_model.get_parameters()[0].get_layout().empty:
+                ov_model.get_parameters()[0].set_layout(Layout('NCHW'))
+            batch_dim = get_batch(ov_model)
             if batch_dim.is_static:
                 batch_size = batch_dim.get_length()
-            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for NCS2
+            ov_compiled_model = core.compile_model(ov_model, device_name='AUTO')  # AUTO selects best available device
             metadata = w.parent / 'metadata.yaml'
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
@@ -339,7 +339,7 @@ class AutoBackend(nn.Module):
             y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
         elif self.xml:  # OpenVINO
             im = im.cpu().numpy()  # FP32
-            y = list(self.executable_network([im]).values())
+            y = list(self.ov_compiled_model([im]).values())
         elif self.engine:  # TensorRT
             if self.dynamic and im.shape != self.bindings['images'].shape:
                 i = self.model.get_binding_index('images')

ultralytics/nn/tasks.py

@@ -601,7 +601,7 @@ def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
 
 
 def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
-    # Parse a YOLO model.yaml dictionary into a PyTorch model
+    """Parse a YOLO model.yaml dictionary into a PyTorch model."""
     import ast
 
     # Args

ultralytics/yolo/cfg/__init__.py

@@ -171,8 +171,8 @@ def check_cfg_mismatch(base: Dict, custom: Dict, e=None):
     If any mismatched keys are found, the function prints out similar keys from the base list and exits the program.
 
     Args:
-        custom (Dict): a dictionary of custom configuration options
-        base (Dict): a dictionary of base configuration options
+        custom (dict): a dictionary of custom configuration options
+        base (dict): a dictionary of base configuration options
     """
     custom = _handle_deprecation(custom)
     base, custom = (set(x.keys()) for x in (base, custom))

ultralytics/yolo/data/augment.py

@@ -642,7 +642,8 @@ class CopyPaste:
 
 
 class Albumentations:
-    # YOLOv8 Albumentations class (optional, only used if package is installed)
+    """YOLOv8 Albumentations class (optional, only used if package is installed)"""
+
     def __init__(self, p=1.0):
         """Initialize the transform object for YOLO bbox formatted params."""
         self.p = p
@@ -819,7 +820,7 @@ def classify_albumentations(
         std=(1.0, 1.0, 1.0),  # IMAGENET_STD
         auto_aug=False,
 ):
-    # YOLOv8 classification Albumentations (optional, only used if package is installed)
+    """YOLOv8 classification Albumentations (optional, only used if package is installed)."""
     prefix = colorstr('albumentations: ')
     try:
         import albumentations as A
@@ -851,7 +852,8 @@ def classify_albumentations(
 
 
 class ClassifyLetterBox:
-    # YOLOv8 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+    """YOLOv8 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])"""
+
     def __init__(self, size=(640, 640), auto=False, stride=32):
         """Resizes image and crops it to center with max dimensions 'h' and 'w'."""
         super().__init__()
@@ -871,7 +873,8 @@ class ClassifyLetterBox:
 
 
 class CenterCrop:
-    # YOLOv8 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
+    """YOLOv8 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])"""
+
     def __init__(self, size=640):
         """Converts an image from numpy array to PyTorch tensor."""
         super().__init__()
@@ -885,7 +888,8 @@ class CenterCrop:
 
 
 class ToTensor:
-    # YOLOv8 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+    """YOLOv8 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])."""
+
     def __init__(self, half=False):
         """Initialize YOLOv8 ToTensor object with optional half-precision support."""
         super().__init__()

ultralytics/yolo/data/build.py

@@ -63,7 +63,7 @@ class _RepeatSampler:
 
 
 def seed_worker(worker_id):  # noqa
-    # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader
+    """Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader."""
     worker_seed = torch.initial_seed() % 2 ** 32
     np.random.seed(worker_seed)
     random.seed(worker_seed)

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -29,7 +29,8 @@ class SourceTypes:
 
 
 class LoadStreams:
-    # YOLOv8 streamloader, i.e. `yolo predict source='rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`
+    """YOLOv8 streamloader, i.e. `yolo predict source='rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`."""
+
     def __init__(self, sources='file.streams', imgsz=640, vid_stride=1):
         """Initialize instance variables and check for consistent input stream shapes."""
         torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
@@ -116,7 +117,8 @@ class LoadStreams:
 
 
 class LoadScreenshots:
-    # YOLOv8 screenshot dataloader, i.e. `yolo predict source=screen`
+    """YOLOv8 screenshot dataloader, i.e. `yolo predict source=screen`."""
+
     def __init__(self, source, imgsz=640):
         """source = [screen_number left top width height] (pixels)."""
         check_requirements('mss')
@@ -158,7 +160,8 @@ class LoadScreenshots:
 
 
 class LoadImages:
-    # YOLOv8 image/video dataloader, i.e. `yolo predict source=image.jpg/vid.mp4`
+    """YOLOv8 image/video dataloader, i.e. `yolo predict source=image.jpg/vid.mp4`."""
+
     def __init__(self, path, imgsz=640, vid_stride=1):
         """Initialize the Dataloader and raise FileNotFoundError if file not found."""
         if isinstance(path, str) and Path(path).suffix == '.txt':  # *.txt file with img/vid/dir on each line

ultralytics/yolo/data/utils.py

@@ -278,7 +278,7 @@ def check_cls_dataset(dataset: str, split=''):
         split (str, optional): The split of the dataset. Either 'val', 'test', or ''. Defaults to ''.
 
     Returns:
-        dict: A dictionary containing the following keys:
+        (dict): A dictionary containing the following keys:
             - 'train' (Path): The directory path containing the training set of the dataset.
             - 'val' (Path): The directory path containing the validation set of the dataset.
             - 'test' (Path): The directory path containing the test set of the dataset.

ultralytics/yolo/engine/exporter.py

@@ -55,6 +55,7 @@ import subprocess
 import time
 import warnings
 from copy import deepcopy
+from datetime import datetime
 from pathlib import Path
 
 import torch
@@ -219,6 +220,7 @@ class Exporter:
             'description': description,
             'author': 'Ultralytics',
             'license': 'AGPL-3.0 https://ultralytics.com/license',
+            'date': datetime.now().isoformat(),
             'version': __version__,
             'stride': int(max(model.stride)),
             'task': model.task,

ultralytics/yolo/engine/results.py

@@ -213,16 +213,18 @@ class Results(SimpleClass):
             assert type(line_width) == int, '`line_width` should be of int type, i.e, line_width=3'
 
         names = self.names
-        annotator = Annotator(deepcopy(self.orig_img if img is None else img),
+        pred_boxes, show_boxes = self.boxes, boxes
+        pred_masks, show_masks = self.masks, masks
+        pred_probs, show_probs = self.probs, probs
+        annotator = Annotator(
+            deepcopy(self.orig_img if img is None else img),
             line_width,
             font_size,
             font,
-                              pil,
+            pil or (pred_probs is not None and show_probs),  # Classify tasks default to pil=True
             example=names)
-        pred_boxes, show_boxes = self.boxes, boxes
-        pred_masks, show_masks = self.masks, masks
-        pred_probs, show_probs = self.probs, probs
-        keypoints = self.keypoints
+
+        # Plot Segment results
         if pred_masks and show_masks:
             if img_gpu is None:
                 img = LetterBox(pred_masks.shape[1:])(image=annotator.result())
@@ -231,6 +233,7 @@ class Results(SimpleClass):
             idx = pred_boxes.cls if pred_boxes else range(len(pred_masks))
             annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=img_gpu)
 
+        # Plot Detect results
         if pred_boxes and show_boxes:
             for d in reversed(pred_boxes):
                 c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item())
@@ -238,12 +241,15 @@ class Results(SimpleClass):
                 label = (f'{name} {conf:.2f}' if conf else name) if labels else None
                 annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
 
+        # Plot Classify results
         if pred_probs is not None and show_probs:
-            text = f"{', '.join(f'{names[j] if names else j} {pred_probs.data[j]:.2f}' for j in pred_probs.top5)}, "
-            annotator.text((32, 32), text, txt_color=(255, 255, 255))  # TODO: allow setting colors
+            text = ',\n'.join(f'{names[j] if names else j} {pred_probs.data[j]:.2f}' for j in pred_probs.top5)
+            x = round(self.orig_shape[0] * 0.03)
+            annotator.text([x, x], text, txt_color=(255, 255, 255))  # TODO: allow setting colors
 
-        if keypoints is not None:
-            for k in reversed(keypoints.data):
+        # Plot Pose results
+        if self.keypoints is not None:
+            for k in reversed(self.keypoints.data):
                 annotator.kpts(k, self.orig_shape, kpt_line=kpt_line)
 
         return annotator.result()

ultralytics/yolo/utils/checks.py

@@ -211,6 +211,7 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
     """
     prefix = colorstr('red', 'bold', 'requirements:')
     check_python()  # check python version
+    check_torchvision()  # check torch-torchvision compatibility
     file = None
     if isinstance(requirements, Path):  # requirements.txt file
         file = requirements.resolve()
@@ -255,6 +256,34 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
     return True
 
 
+def check_torchvision():
+    """
+    Checks the installed versions of PyTorch and Torchvision to ensure they're compatible.
+
+    This function checks the installed versions of PyTorch and Torchvision, and warns if they're incompatible according
+    to the provided compatibility table based on https://github.com/pytorch/vision#installation. The
+    compatibility table is a dictionary where the keys are PyTorch versions and the values are lists of compatible
+    Torchvision versions.
+    """
+
+    import torchvision
+
+    # Compatibility table
+    compatibility_table = {'2.0': ['0.15'], '1.13': ['0.14'], '1.12': ['0.13']}
+
+    # Extract only the major and minor versions
+    v_torch = '.'.join(torch.__version__.split('+')[0].split('.')[:2])
+    v_torchvision = '.'.join(torchvision.__version__.split('+')[0].split('.')[:2])
+
+    if v_torch in compatibility_table:
+        compatible_versions = compatibility_table[v_torch]
+        if all(pkg.parse_version(v_torchvision) != pkg.parse_version(v) for v in compatible_versions):
+            print(f'WARNING ⚠️ torchvision=={v_torchvision} is incompatible with torch=={v_torch}.\n'
+                  f"Run 'pip install torchvision=={compatible_versions[0]}' to fix torchvision or "
+                  "'pip install -U torch torchvision' to update both.\n"
+                  'For a full compatibility table see https://github.com/pytorch/vision#installation')
+
+
 def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
     """Check file(s) for acceptable suffix."""
     if file and suffix:
@@ -402,7 +431,7 @@ def check_amp(model):
 
 
 def git_describe(path=ROOT):  # path must be a directory
-    # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
+    """Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe."""
     try:
         assert (Path(path) / '.git').is_dir()
         return subprocess.check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]

ultralytics/yolo/utils/files.py

@@ -91,7 +91,7 @@ def get_latest_run(search_dir='.'):
 
 
 def make_dirs(dir='new_dir/'):
-    # Create folders
+    """Create directories."""
     dir = Path(dir)
     if dir.exists():
         shutil.rmtree(dir)  # delete dir

ultralytics/yolo/utils/ops.py

@@ -55,12 +55,17 @@ class Profile(contextlib.ContextDecorator):
         return time.time()
 
 
-def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
-    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
-    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
-    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
-    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
-    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
+def coco80_to_coco91_class():  #
+    """
+    Converts 80-index (val2014) to 91-index (paper).
+    For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.
+
+    Example:
+        a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
+        b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
+        x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
+        x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
+    """
     return [
         1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,

ultralytics/yolo/utils/patches.py

@@ -34,7 +34,7 @@ _torch_save = torch.save  # copy to avoid recursion errors
 
 
 def torch_save(*args, **kwargs):
-    # Use dill (if exists) to serialize the lambda functions where pickle does not do this
+    """Use dill (if exists) to serialize the lambda functions where pickle does not do this."""
     try:
         import dill as pickle
     except ImportError:

ultralytics/yolo/utils/plotting.py

@@ -21,7 +21,8 @@ from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
 
 
 class Colors:
-    # Ultralytics color palette https://ultralytics.com/
+    """Ultralytics color palette https://ultralytics.com/."""
+
     def __init__(self):
         """Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
         hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
@@ -48,7 +49,8 @@ colors = Colors()  # create instance for 'from utils.plots import colors'
 
 
 class Annotator:
-    # YOLOv8 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
+    """YOLOv8 Annotator for train/val mosaics and jpgs and detect/hub inference annotations."""
+
     def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
         """Initialize the Annotator class with image and line width along with color palette for keypoints and limbs."""
         assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
@@ -204,6 +206,13 @@ class Annotator:
                 self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
                 # Using `txt_color` for background and draw fg with white color
                 txt_color = (255, 255, 255)
+            if '\n' in text:
+                lines = text.split('\n')
+                _, h = self.font.getsize(text)
+                for line in lines:
+                    self.draw.text(xy, line, fill=txt_color, font=self.font)
+                    xy[1] += h
+            else:
                 self.draw.text(xy, text, fill=txt_color, font=self.font)
         else:
             if box_style:
@@ -310,7 +319,7 @@ def plot_images(images,
                 fname='images.jpg',
                 names=None,
                 on_plot=None):
-    # Plot image grid with labels
+    """Plot image grid with labels."""
     if isinstance(images, torch.Tensor):
         images = images.cpu().float().numpy()
     if isinstance(cls, torch.Tensor):

ultralytics/yolo/utils/torch_utils.py

@@ -232,7 +232,7 @@ def get_flops(model, imgsz=640):
 
 
 def get_flops_with_torch_profiler(model, imgsz=640):
-    # Compute model FLOPs (thop alternative)
+    """Compute model FLOPs (thop alternative)."""
     model = de_parallel(model)
     p = next(model.parameters())
     stride = (max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32) * 2  # max stride