ultralytics 8.0.80 single-line docstring fixes (#2060)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.79'
+__version__ = '8.0.80'
 
 from ultralytics.hub import start
 from ultralytics.yolo.engine.model import YOLO

ultralytics/hub/__init__.py

@@ -54,7 +54,7 @@ model.train()""")
 
 
 def reset_model(model_id=''):
-    # Reset a trained model to an untrained state
+    """Reset a trained model to an untrained state."""
     r = requests.post('https://api.ultralytics.com/model-reset', json={'apiKey': Auth().api_key, 'modelId': model_id})
     if r.status_code == 200:
         LOGGER.info(f'{PREFIX}Model reset successfully')
@@ -63,13 +63,13 @@ def reset_model(model_id=''):
 
 
 def export_fmts_hub():
-    # Returns a list of HUB-supported export formats
+    """Returns a list of HUB-supported export formats."""
     from ultralytics.yolo.engine.exporter import export_formats
     return list(export_formats()['Argument'][1:]) + ['ultralytics_tflite', 'ultralytics_coreml']
 
 
 def export_model(model_id='', format='torchscript'):
-    # Export a model to all formats
+    """Export a model to all formats."""
     assert format in export_fmts_hub(), f"Unsupported export format '{format}', valid formats are {export_fmts_hub()}"
     r = requests.post('https://api.ultralytics.com/export',
                       json={
@@ -81,7 +81,7 @@ def export_model(model_id='', format='torchscript'):
 
 
 def get_export(model_id='', format='torchscript'):
-    # Get an exported model dictionary with download URL
+    """Get an exported model dictionary with download URL."""
     assert format in export_fmts_hub(), f"Unsupported export format '{format}', valid formats are {export_fmts_hub()}"
     r = requests.post('https://api.ultralytics.com/get-export',
                       json={

ultralytics/hub/session.py

@@ -124,7 +124,7 @@ class HUBTrainingSession:
                     'device': data['device'],
                     'cache': data['cache'],
                     'data': data['data']}
-                self.model_file = data.get('cfg', data['weights'])
+                self.model_file = data.get('cfg') or data.get('weights')  # cfg for pretrained=False
                 self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False)  # YOLOv5->YOLOv5u
             elif data['status'] == 'training':  # existing model to resume training
                 self.train_args = {'data': data['data'], 'resume': True}

ultralytics/nn/autobackend.py

@@ -21,11 +21,11 @@ from ultralytics.yolo.utils.ops import xywh2xyxy
 
 
 def check_class_names(names):
-    # Check class names. Map imagenet class codes to human-readable names if required. Convert lists to dicts.
+    """Check class names. Map imagenet class codes to human-readable names if required. Convert lists to dicts."""
     if isinstance(names, list):  # names is a list
         names = dict(enumerate(names))  # convert to dict
     if isinstance(names, dict):
-        # convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True'
+        # Convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True'
         names = {int(k): str(v) for k, v in names.items()}
         n = len(names)
         if max(names.keys()) >= n:
@@ -229,7 +229,7 @@ class AutoBackend(nn.Module):
             interpreter.allocate_tensors()  # allocate
             input_details = interpreter.get_input_details()  # inputs
             output_details = interpreter.get_output_details()  # outputs
-            # load metadata
+            # Load metadata
             with contextlib.suppress(zipfile.BadZipFile):
                 with zipfile.ZipFile(w, 'r') as model:
                     meta_file = model.namelist()[0]

ultralytics/nn/autoshape.py

@@ -24,7 +24,7 @@ from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode
 
 
 class AutoShape(nn.Module):
-    # YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    """YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS."""
     conf = 0.25  # NMS confidence threshold
     iou = 0.45  # NMS IoU threshold
     agnostic = False  # NMS class-agnostic
@@ -47,7 +47,7 @@ class AutoShape(nn.Module):
             m.export = True  # do not output loss values
 
     def _apply(self, fn):
-        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        """Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers."""
         self = super()._apply(fn)
         if self.pt:
             m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
@@ -59,7 +59,7 @@ class AutoShape(nn.Module):
 
     @smart_inference_mode()
     def forward(self, ims, size=640, augment=False, profile=False):
-        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
+        """Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:."""
         #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
         #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
         #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
@@ -202,7 +202,7 @@ class Detections:
         return self.ims
 
     def pandas(self):
-        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
+        """Return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])."""
         import pandas
         new = copy(self)  # return copy
         ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
@@ -213,7 +213,7 @@ class Detections:
         return new
 
     def tolist(self):
-        # return a list of Detections objects, i.e. 'for result in results.tolist():'
+        """Return a list of Detections objects, i.e. 'for result in results.tolist():'."""
         r = range(self.n)  # iterable
         x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
         # for d in x:

ultralytics/nn/modules.py

@@ -12,7 +12,7 @@ from ultralytics.yolo.utils.tal import dist2bbox, make_anchors
 
 
 def autopad(k, p=None, d=1):  # kernel, padding, dilation
-    # Pad to 'same' shape outputs
+    """Pad to 'same' shape outputs."""
     if d > 1:
         k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
     if p is None:
@@ -21,7 +21,7 @@ def autopad(k, p=None, d=1):  # kernel, padding, dilation
 
 
 class Conv(nn.Module):
-    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
+    """Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""
     default_act = nn.SiLU()  # default activation
 
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
@@ -38,19 +38,21 @@ class Conv(nn.Module):
 
 
 class DWConv(Conv):
-    # Depth-wise convolution
+    """Depth-wise convolution."""
+
     def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
         super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
 
 
 class DWConvTranspose2d(nn.ConvTranspose2d):
-    # Depth-wise transpose convolution
+    """Depth-wise transpose convolution."""
+
     def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
         super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
 
 
 class ConvTranspose(nn.Module):
-    # Convolution transpose 2d layer
+    """Convolution transpose 2d layer."""
     default_act = nn.SiLU()  # default activation
 
     def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True):
@@ -67,8 +69,11 @@ class ConvTranspose(nn.Module):
 
 
 class DFL(nn.Module):
-    # Integral module of Distribution Focal Loss (DFL)
+    """
-    # Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
+    Integral module of Distribution Focal Loss (DFL).
+    Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
+    """
+
     def __init__(self, c1=16):
         super().__init__()
         self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
@@ -83,7 +88,8 @@ class DFL(nn.Module):
 
 
 class TransformerLayer(nn.Module):
-    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+    """Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)."""
+
     def __init__(self, c, num_heads):
         super().__init__()
         self.q = nn.Linear(c, c, bias=False)
@@ -100,7 +106,8 @@ class TransformerLayer(nn.Module):
 
 
 class TransformerBlock(nn.Module):
-    # Vision Transformer https://arxiv.org/abs/2010.11929
+    """Vision Transformer https://arxiv.org/abs/2010.11929."""
+
     def __init__(self, c1, c2, num_heads, num_layers):
         super().__init__()
         self.conv = None
@@ -119,7 +126,8 @@ class TransformerBlock(nn.Module):
 
 
 class Bottleneck(nn.Module):
-    # Standard bottleneck
+    """Standard bottleneck."""
+
     def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, groups, kernels, expand
         super().__init__()
         c_ = int(c2 * e)  # hidden channels
@@ -132,7 +140,8 @@ class Bottleneck(nn.Module):
 
 
 class BottleneckCSP(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    """CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
         super().__init__()
         c_ = int(c2 * e)  # hidden channels
@@ -151,7 +160,8 @@ class BottleneckCSP(nn.Module):
 
 
 class C3(nn.Module):
-    # CSP Bottleneck with 3 convolutions
+    """CSP Bottleneck with 3 convolutions."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
         super().__init__()
         c_ = int(c2 * e)  # hidden channels
@@ -165,7 +175,8 @@ class C3(nn.Module):
 
 
 class C2(nn.Module):
-    # CSP Bottleneck with 2 convolutions
+    """CSP Bottleneck with 2 convolutions."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
         super().__init__()
         self.c = int(c2 * e)  # hidden channels
@@ -180,7 +191,8 @@ class C2(nn.Module):
 
 
 class C2f(nn.Module):
-    # CSP Bottleneck with 2 convolutions
+    """CSP Bottleneck with 2 convolutions."""
+
     def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
         super().__init__()
         self.c = int(c2 * e)  # hidden channels
@@ -200,7 +212,8 @@ class C2f(nn.Module):
 
 
 class ChannelAttention(nn.Module):
-    # Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet
+    """Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""
+
     def __init__(self, channels: int) -> None:
         super().__init__()
         self.pool = nn.AdaptiveAvgPool2d(1)
@@ -212,7 +225,8 @@ class ChannelAttention(nn.Module):
 
 
 class SpatialAttention(nn.Module):
-    # Spatial-attention module
+    """Spatial-attention module."""
+
     def __init__(self, kernel_size=7):
         super().__init__()
         assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
@@ -225,7 +239,8 @@ class SpatialAttention(nn.Module):
 
 
 class CBAM(nn.Module):
-    # Convolutional Block Attention Module
+    """Convolutional Block Attention Module."""
+
     def __init__(self, c1, kernel_size=7):  # ch_in, kernels
         super().__init__()
         self.channel_attention = ChannelAttention(c1)
@@ -236,7 +251,8 @@ class CBAM(nn.Module):
 
 
 class C1(nn.Module):
-    # CSP Bottleneck with 1 convolution
+    """CSP Bottleneck with 1 convolution."""
+
     def __init__(self, c1, c2, n=1):  # ch_in, ch_out, number
         super().__init__()
         self.cv1 = Conv(c1, c2, 1, 1)
@@ -248,7 +264,8 @@ class C1(nn.Module):
 
 
 class C3x(C3):
-    # C3 module with cross-convolutions
+    """C3 module with cross-convolutions."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         super().__init__(c1, c2, n, shortcut, g, e)
         self.c_ = int(c2 * e)
@@ -256,7 +273,8 @@ class C3x(C3):
 
 
 class C3TR(C3):
-    # C3 module with TransformerBlock()
+    """C3 module with TransformerBlock()."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         super().__init__(c1, c2, n, shortcut, g, e)
         c_ = int(c2 * e)
@@ -264,7 +282,8 @@ class C3TR(C3):
 
 
 class C3Ghost(C3):
-    # C3 module with GhostBottleneck()
+    """C3 module with GhostBottleneck()."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         super().__init__(c1, c2, n, shortcut, g, e)
         c_ = int(c2 * e)  # hidden channels
@@ -272,7 +291,8 @@ class C3Ghost(C3):
 
 
 class SPP(nn.Module):
-    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
+    """Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729."""
+
     def __init__(self, c1, c2, k=(5, 9, 13)):
         super().__init__()
         c_ = c1 // 2  # hidden channels
@@ -286,7 +306,8 @@ class SPP(nn.Module):
 
 
 class SPPF(nn.Module):
-    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+    """Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher."""
+
     def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
         super().__init__()
         c_ = c1 // 2  # hidden channels
@@ -302,7 +323,8 @@ class SPPF(nn.Module):
 
 
 class Focus(nn.Module):
-    # Focus wh information into c-space
+    """Focus wh information into c-space."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
         super().__init__()
         self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
@@ -314,7 +336,8 @@ class Focus(nn.Module):
 
 
 class GhostConv(nn.Module):
-    # Ghost Convolution https://github.com/huawei-noah/ghostnet
+    """Ghost Convolution https://github.com/huawei-noah/ghostnet."""
+
     def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
         super().__init__()
         c_ = c2 // 2  # hidden channels
@@ -327,7 +350,8 @@ class GhostConv(nn.Module):
 
 
 class GhostBottleneck(nn.Module):
-    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+    """Ghost Bottleneck https://github.com/huawei-noah/ghostnet."""
+
     def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
         super().__init__()
         c_ = c2 // 2
@@ -343,7 +367,8 @@ class GhostBottleneck(nn.Module):
 
 
 class Concat(nn.Module):
-    # Concatenate a list of tensors along dimension
+    """Concatenate a list of tensors along dimension."""
+
     def __init__(self, dimension=1):
         super().__init__()
         self.d = dimension
@@ -353,7 +378,8 @@ class Concat(nn.Module):
 
 
 class Proto(nn.Module):
-    # YOLOv8 mask Proto module for segmentation models
+    """YOLOv8 mask Proto module for segmentation models."""
+
     def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
         super().__init__()
         self.cv1 = Conv(c1, c_, k=3)
@@ -366,7 +392,8 @@ class Proto(nn.Module):
 
 
 class Ensemble(nn.ModuleList):
-    # Ensemble of models
+    """Ensemble of models."""
+
     def __init__(self):
         super().__init__()
 
@@ -382,7 +409,7 @@ class Ensemble(nn.ModuleList):
 
 
 class Detect(nn.Module):
-    # YOLOv8 Detect head for detection models
+    """YOLOv8 Detect head for detection models."""
     dynamic = False  # force grid reconstruction
     export = False  # export mode
     shape = None
@@ -423,7 +450,7 @@ class Detect(nn.Module):
         return y if self.export else (y, x)
 
     def bias_init(self):
-        # Initialize Detect() biases, WARNING: requires stride availability
+        """Initialize Detect() biases, WARNING: requires stride availability."""
         m = self  # self.model[-1]  # Detect() module
         # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
         # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
@@ -433,7 +460,8 @@ class Detect(nn.Module):
 
 
 class Segment(Detect):
-    # YOLOv8 Segment head for segmentation models
+    """YOLOv8 Segment head for segmentation models."""
+
     def __init__(self, nc=80, nm=32, npr=256, ch=()):
         super().__init__(nc, ch)
         self.nm = nm  # number of masks
@@ -456,7 +484,8 @@ class Segment(Detect):
 
 
 class Pose(Detect):
-    # YOLOv8 Pose head for keypoints models
+    """YOLOv8 Pose head for keypoints models."""
+
     def __init__(self, nc=80, kpt_shape=(17, 3), ch=()):
         super().__init__(nc, ch)
         self.kpt_shape = kpt_shape  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
@@ -486,7 +515,8 @@ class Pose(Detect):
 
 
 class Classify(nn.Module):
-    # YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    """YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
         super().__init__()
         c_ = 1280  # efficientnet_b0 size

ultralytics/nn/tasks.py

@@ -167,7 +167,8 @@ class BaseModel(nn.Module):
 
 
 class DetectionModel(BaseModel):
-    # YOLOv8 detection model
+    """YOLOv8 detection model."""
+
     def __init__(self, cfg='yolov8n.yaml', ch=3, nc=None, verbose=True):  # model, input channels, number of classes
         super().__init__()
         self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict
@@ -218,7 +219,7 @@ class DetectionModel(BaseModel):
 
     @staticmethod
     def _descale_pred(p, flips, scale, img_size, dim=1):
-        # de-scale predictions following augmented inference (inverse operation)
+        """De-scale predictions following augmented inference (inverse operation)."""
         p[:, :4] /= scale  # de-scale
         x, y, wh, cls = p.split((1, 1, 2, p.shape[dim] - 4), dim)
         if flips == 2:
@@ -228,7 +229,7 @@ class DetectionModel(BaseModel):
         return torch.cat((x, y, wh, cls), dim)
 
     def _clip_augmented(self, y):
-        # Clip YOLOv5 augmented inference tails
+        """Clip YOLOv5 augmented inference tails."""
         nl = self.model[-1].nl  # number of detection layers (P3-P5)
         g = sum(4 ** x for x in range(nl))  # grid points
         e = 1  # exclude layer count
@@ -240,7 +241,8 @@ class DetectionModel(BaseModel):
 
 
 class SegmentationModel(DetectionModel):
-    # YOLOv8 segmentation model
+    """YOLOv8 segmentation model."""
+
     def __init__(self, cfg='yolov8n-seg.yaml', ch=3, nc=None, verbose=True):
         super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
 
@@ -249,7 +251,8 @@ class SegmentationModel(DetectionModel):
 
 
 class PoseModel(DetectionModel):
-    # YOLOv8 pose model
+    """YOLOv8 pose model."""
+
     def __init__(self, cfg='yolov8n-pose.yaml', ch=3, nc=None, data_kpt_shape=(None, None), verbose=True):
         if not isinstance(cfg, dict):
             cfg = yaml_model_load(cfg)  # load model YAML
@@ -260,7 +263,8 @@ class PoseModel(DetectionModel):
 
 
 class ClassificationModel(BaseModel):
-    # YOLOv8 classification model
+    """YOLOv8 classification model."""
+
     def __init__(self,
                  cfg=None,
                  model=None,
@@ -272,7 +276,7 @@ class ClassificationModel(BaseModel):
         self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg, ch, nc, verbose)
 
     def _from_detection_model(self, model, nc=1000, cutoff=10):
-        # Create a YOLOv5 classification model from a YOLOv5 detection model
+        """Create a YOLOv5 classification model from a YOLOv5 detection model."""
         from ultralytics.nn.autobackend import AutoBackend
         if isinstance(model, AutoBackend):
             model = model.model  # unwrap DetectMultiBackend
@@ -304,7 +308,7 @@ class ClassificationModel(BaseModel):
 
     @staticmethod
     def reshape_outputs(model, nc):
-        # Update a TorchVision classification model to class count 'n' if required
+        """Update a TorchVision classification model to class count 'n' if required."""
         name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1]  # last module
         if isinstance(m, Classify):  # YOLO Classify() head
             if m.linear.out_features != nc:
@@ -363,7 +367,7 @@ def torch_safe_load(weight):
 
 
 def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
-    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+    """Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a."""
 
     ensemble = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
@@ -403,7 +407,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
 
 
 def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
-    # Loads a single model weights
+    """Loads a single model weights."""
     ckpt, weight = torch_safe_load(weight)  # load ckpt
     args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
     model = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
@@ -546,7 +550,7 @@ def guess_model_task(model):
     """
 
     def cfg2task(cfg):
-        # Guess from YAML dictionary
+        """Guess from YAML dictionary."""
         m = cfg['head'][-1][-2].lower()  # output module name
         if m in ('classify', 'classifier', 'cls', 'fc'):
             return 'classify'

ultralytics/tracker/trackers/basetrack.py

@@ -27,7 +27,7 @@ class BaseTrack:
     frame_id = 0
     time_since_update = 0
 
-    # multi-camera
+    # Multi-camera
     location = (np.inf, np.inf)
 
     @property

ultralytics/tracker/trackers/bot_sort.py

@@ -100,7 +100,7 @@ class BOTSORT(BYTETracker):
         self.appearance_thresh = args.appearance_thresh
 
         if args.with_reid:
-            # haven't supported BoT-SORT(reid) yet
+            # Haven't supported BoT-SORT(reid) yet
             self.encoder = None
         # self.gmc = GMC(method=args.cmc_method, verbose=[args.name, args.ablation])
         self.gmc = GMC(method=args.cmc_method)

ultralytics/tracker/trackers/byte_tracker.py

@@ -11,8 +11,7 @@ class STrack(BaseTrack):
     shared_kalman = KalmanFilterXYAH()
 
     def __init__(self, tlwh, score, cls):
-
+        """wait activate."""
-        # wait activate
         self._tlwh = np.asarray(self.tlbr_to_tlwh(tlwh[:-1]), dtype=np.float32)
         self.kalman_filter = None
         self.mean, self.covariance = None, None
@@ -62,7 +61,7 @@ class STrack(BaseTrack):
                 stracks[i].covariance = cov
 
     def activate(self, kalman_filter, frame_id):
-        """Start a new tracklet"""
+        """Start a new tracklet."""
         self.kalman_filter = kalman_filter
         self.track_id = self.next_id()
         self.mean, self.covariance = self.kalman_filter.initiate(self.convert_coords(self._tlwh))
@@ -179,7 +178,7 @@ class BYTETracker:
 
         scores = results.conf
         bboxes = results.xyxy
-        # add index
+        # Add index
         bboxes = np.concatenate([bboxes, np.arange(len(bboxes)).reshape(-1, 1)], axis=-1)
         cls = results.cls
 
@@ -196,7 +195,7 @@ class BYTETracker:
         cls_second = cls[inds_second]
 
         detections = self.init_track(dets, scores_keep, cls_keep, img)
-        """ Add newly detected tracklets to tracked_stracks"""
+        # Add newly detected tracklets to tracked_stracks
         unconfirmed = []
         tracked_stracks = []  # type: list[STrack]
         for track in self.tracked_stracks:
@@ -204,7 +203,7 @@ class BYTETracker:
                 unconfirmed.append(track)
             else:
                 tracked_stracks.append(track)
-        """ Step 2: First association, with high score detection boxes"""
+        # Step 2: First association, with high score detection boxes
         strack_pool = self.joint_stracks(tracked_stracks, self.lost_stracks)
         # Predict the current location with KF
         self.multi_predict(strack_pool)
@@ -225,7 +224,7 @@ class BYTETracker:
             else:
                 track.re_activate(det, self.frame_id, new_id=False)
                 refind_stracks.append(track)
-        """ Step 3: Second association, with low score detection boxes"""
+        # Step 3: Second association, with low score detection boxes
         # association the untrack to the low score detections
         detections_second = self.init_track(dets_second, scores_second, cls_second, img)
         r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
@@ -247,7 +246,7 @@ class BYTETracker:
             if track.state != TrackState.Lost:
                 track.mark_lost()
                 lost_stracks.append(track)
-        """Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
+        # Deal with unconfirmed tracks, usually tracks with only one beginning frame
         detections = [detections[i] for i in u_detection]
         dists = self.get_dists(unconfirmed, detections)
         matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
@@ -258,14 +257,14 @@ class BYTETracker:
             track = unconfirmed[it]
             track.mark_removed()
             removed_stracks.append(track)
-        """ Step 4: Init new stracks"""
+        # Step 4: Init new stracks
         for inew in u_detection:
             track = detections[inew]
             if track.score < self.args.new_track_thresh:
                 continue
             track.activate(self.kalman_filter, self.frame_id)
             activated_starcks.append(track)
-        """ Step 5: Update state"""
+        # Step 5: Update state
         for track in self.lost_stracks:
             if self.frame_id - track.end_frame > self.max_time_lost:
                 track.mark_removed()
@@ -320,7 +319,7 @@ class BYTETracker:
 
     @staticmethod
     def sub_stracks(tlista, tlistb):
-        """ DEPRECATED CODE in https://github.com/ultralytics/ultralytics/pull/1890/
+        """DEPRECATED CODE in https://github.com/ultralytics/ultralytics/pull/1890/
         stracks = {t.track_id: t for t in tlista}
         for t in tlistb:
             tid = t.track_id

ultralytics/tracker/utils/gmc.py

@@ -83,8 +83,7 @@ class GMC:
             return np.eye(2, 3)
 
     def applyEcc(self, raw_frame, detections=None):
-
+        """Initialize."""
-        # Initialize
         height, width, _ = raw_frame.shape
         frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
         H = np.eye(2, 3, dtype=np.float32)
@@ -116,8 +115,7 @@ class GMC:
         return H
 
     def applyFeatures(self, raw_frame, detections=None):
-
+        """Initialize."""
-        # Initialize
         height, width, _ = raw_frame.shape
         frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
         H = np.eye(2, 3)
@@ -129,7 +127,7 @@ class GMC:
             width = width // self.downscale
             height = height // self.downscale
 
-        # find the keypoints
+        # Find the keypoints
         mask = np.zeros_like(frame)
         # mask[int(0.05 * height): int(0.95 * height), int(0.05 * width): int(0.95 * width)] = 255
         mask[int(0.02 * height):int(0.98 * height), int(0.02 * width):int(0.98 * width)] = 255
@@ -140,7 +138,7 @@ class GMC:
 
         keypoints = self.detector.detect(frame, mask)
 
-        # compute the descriptors
+        # Compute the descriptors
         keypoints, descriptors = self.extractor.compute(frame, keypoints)
 
         # Handle first frame
@@ -243,7 +241,7 @@ class GMC:
         return H
 
     def applySparseOptFlow(self, raw_frame, detections=None):
-        # Initialize
+        """Initialize."""
         # t0 = time.time()
         height, width, _ = raw_frame.shape
         frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
@@ -254,7 +252,7 @@ class GMC:
             # frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
             frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
 
-        # find the keypoints
+        # Find the keypoints
         keypoints = cv2.goodFeaturesToTrack(frame, mask=None, **self.feature_params)
 
         # Handle first frame
@@ -268,10 +266,10 @@ class GMC:
 
             return H
 
-        # find correspondences
+        # Find correspondences
         matchedKeypoints, status, err = cv2.calcOpticalFlowPyrLK(self.prevFrame, frame, self.prevKeyPoints, None)
 
-        # leave good correspondences only
+        # Leave good correspondences only
         prevPoints = []
         currPoints = []
 

ultralytics/tracker/utils/matching.py

@@ -8,6 +8,7 @@ from .kalman_filter import chi2inv95
 
 try:
     import lap  # for linear_assignment
+
     assert lap.__version__  # verify package is not directory
 except (ImportError, AssertionError, AttributeError):
     from ultralytics.yolo.utils.checks import check_requirements
@@ -45,7 +46,7 @@ def _indices_to_matches(cost_matrix, indices, thresh):
 
 
 def linear_assignment(cost_matrix, thresh, use_lap=True):
-    # Linear assignment implementations with scipy and lap.lapjv
+    """Linear assignment implementations with scipy and lap.lapjv."""
     if cost_matrix.size == 0:
         return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
 

ultralytics/yolo/cfg/__init__.py

@@ -400,5 +400,5 @@ def copy_default_cfg():
 
 
 if __name__ == '__main__':
-    # entrypoint(debug='yolo predict model=yolov8n.pt')
+    # Example Usage: entrypoint(debug='yolo predict model=yolov8n.pt')
     entrypoint(debug='')

ultralytics/yolo/data/augment.py

@@ -66,7 +66,7 @@ class Compose:
 
 
 class BaseMixTransform:
-    """This implementation is from mmyolo"""
+    """This implementation is from mmyolo."""
 
     def __init__(self, dataset, pre_transform=None, p=0.0) -> None:
         self.dataset = dataset
@@ -77,12 +77,12 @@ class BaseMixTransform:
         if random.uniform(0, 1) > self.p:
             return labels
 
-        # get index of one or three other images
+        # Get index of one or three other images
         indexes = self.get_indexes()
         if isinstance(indexes, int):
             indexes = [indexes]
 
-        # get images information will be used for Mosaic or MixUp
+        # Get images information will be used for Mosaic or MixUp
         mix_labels = [self.dataset.get_label_info(i) for i in indexes]
 
         if self.pre_transform is not None:
@@ -132,7 +132,7 @@ class Mosaic(BaseMixTransform):
             img = labels_patch['img']
             h, w = labels_patch.pop('resized_shape')
 
-            # place img in img4
+            # Place img in img4
             if i == 0:  # top left
                 img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
                 x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
@@ -158,7 +158,7 @@ class Mosaic(BaseMixTransform):
         return final_labels
 
     def _update_labels(self, labels, padw, padh):
-        """Update labels"""
+        """Update labels."""
         nh, nw = labels['img'].shape[:2]
         labels['instances'].convert_bbox(format='xyxy')
         labels['instances'].denormalize(nw, nh)
@@ -193,7 +193,7 @@ class MixUp(BaseMixTransform):
         return random.randint(0, len(self.dataset) - 1)
 
     def _mix_transform(self, labels):
-        # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
+        """Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf."""
         r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
         labels2 = labels['mix_labels'][0]
         labels['img'] = (labels['img'] * r + labels2['img'] * (1 - r)).astype(np.uint8)
@@ -217,12 +217,12 @@ class RandomPerspective:
         self.scale = scale
         self.shear = shear
         self.perspective = perspective
-        # mosaic border
+        # Mosaic border
         self.border = border
         self.pre_transform = pre_transform
 
     def affine_transform(self, img, border):
-        # Center
+        """Center."""
         C = np.eye(3, dtype=np.float32)
 
         C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
@@ -253,7 +253,7 @@ class RandomPerspective:
 
         # Combined rotation matrix
         M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
-        # affine image
+        # Affine image
         if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
             if self.perspective:
                 img = cv2.warpPerspective(img, M, dsize=self.size, borderValue=(114, 114, 114))
@@ -281,7 +281,7 @@ class RandomPerspective:
         xy = xy @ M.T  # transform
         xy = (xy[:, :2] / xy[:, 2:3] if self.perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
 
-        # create new boxes
+        # Create new boxes
         x = xy[:, [0, 2, 4, 6]]
         y = xy[:, [1, 3, 5, 7]]
         return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1)), dtype=bboxes.dtype).reshape(4, n).T
@@ -348,7 +348,7 @@ class RandomPerspective:
         img = labels['img']
         cls = labels['cls']
         instances = labels.pop('instances')
-        # make sure the coord formats are right
+        # Make sure the coord formats are right
         instances.convert_bbox(format='xyxy')
         instances.denormalize(*img.shape[:2][::-1])
 
@@ -362,19 +362,19 @@ class RandomPerspective:
 
         segments = instances.segments
         keypoints = instances.keypoints
-        # update bboxes if there are segments.
+        # Update bboxes if there are segments.
         if len(segments):
             bboxes, segments = self.apply_segments(segments, M)
 
         if keypoints is not None:
             keypoints = self.apply_keypoints(keypoints, M)
         new_instances = Instances(bboxes, segments, keypoints, bbox_format='xyxy', normalized=False)
-        # clip
+        # Clip
         new_instances.clip(*self.size)
 
-        # filter instances
+        # Filter instances
         instances.scale(scale_w=scale, scale_h=scale, bbox_only=True)
-        # make the bboxes have the same scale with new_bboxes
+        # Make the bboxes have the same scale with new_bboxes
         i = self.box_candidates(box1=instances.bboxes.T,
                                 box2=new_instances.bboxes.T,
                                 area_thr=0.01 if len(segments) else 0.10)
@@ -441,7 +441,7 @@ class RandomFlip:
         if self.direction == 'horizontal' and random.random() < self.p:
             img = np.fliplr(img)
             instances.fliplr(w)
-            # for keypoints
+            # For keypoints
             if self.flip_idx is not None and instances.keypoints is not None:
                 instances.keypoints = np.ascontiguousarray(instances.keypoints[:, self.flip_idx, :])
         labels['img'] = np.ascontiguousarray(img)
@@ -450,7 +450,7 @@ class RandomFlip:
 
 
 class LetterBox:
-    """Resize image and padding for detection, instance segmentation, pose"""
+    """Resize image and padding for detection, instance segmentation, pose."""
 
     def __init__(self, new_shape=(640, 640), auto=False, scaleFill=False, scaleup=True, stride=32):
         self.new_shape = new_shape
@@ -505,7 +505,7 @@ class LetterBox:
             return img
 
     def _update_labels(self, labels, ratio, padw, padh):
-        """Update labels"""
+        """Update labels."""
         labels['instances'].convert_bbox(format='xyxy')
         labels['instances'].denormalize(*labels['img'].shape[:2][::-1])
         labels['instances'].scale(*ratio)
@@ -519,7 +519,7 @@ class CopyPaste:
         self.p = p
 
     def __call__(self, labels):
-        # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
+        """Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)."""
         im = labels['img']
         cls = labels['cls']
         h, w = im.shape[:2]
@@ -531,7 +531,7 @@ class CopyPaste:
             _, w, _ = im.shape  # height, width, channels
             im_new = np.zeros(im.shape, np.uint8)
 
-            # calculate ioa first then select indexes randomly
+            # Calculate ioa first then select indexes randomly
             ins_flip = deepcopy(instances)
             ins_flip.fliplr(w)
 
@@ -641,7 +641,7 @@ class Format:
         labels['bboxes'] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
         if self.return_keypoint:
             labels['keypoints'] = torch.from_numpy(instances.keypoints)
-        # then we can use collate_fn
+        # Then we can use collate_fn
         if self.batch_idx:
             labels['batch_idx'] = torch.zeros(nl)
         return labels
@@ -654,7 +654,7 @@ class Format:
         return img
 
     def _format_segments(self, instances, cls, w, h):
-        """convert polygon points to bitmap"""
+        """convert polygon points to bitmap."""
         segments = instances.segments
         if self.mask_overlap:
             masks, sorted_idx = polygons2masks_overlap((h, w), segments, downsample_ratio=self.mask_ratio)

ultralytics/yolo/data/base.py

@@ -70,7 +70,7 @@ class BaseDataset(Dataset):
 
         self.ni = len(self.labels)
 
-        # rect stuff
+        # Rect stuff
         self.rect = rect
         self.batch_size = batch_size
         self.stride = stride
@@ -79,13 +79,13 @@ class BaseDataset(Dataset):
             assert self.batch_size is not None
             self.set_rectangle()
 
-        # cache stuff
+        # Cache stuff
         self.ims = [None] * self.ni
         self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
         if cache:
             self.cache_images(cache)
 
-        # transforms
+        # Transforms
         self.transforms = self.build_transforms(hyp=hyp)
 
     def get_img_files(self, img_path):
@@ -96,13 +96,13 @@ class BaseDataset(Dataset):
                 p = Path(p)  # os-agnostic
                 if p.is_dir():  # dir
                     f += glob.glob(str(p / '**' / '*.*'), recursive=True)
-                    # f = list(p.rglob('*.*'))  # pathlib
+                    # F = list(p.rglob('*.*'))  # pathlib
                 elif p.is_file():  # file
                     with open(p) as t:
                         t = t.read().strip().splitlines()
                         parent = str(p.parent) + os.sep
                         f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
-                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
+                        # F += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
                 else:
                     raise FileNotFoundError(f'{self.prefix}{p} does not exist')
             im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
@@ -113,7 +113,7 @@ class BaseDataset(Dataset):
         return im_files
 
     def update_labels(self, include_class: Optional[list]):
-        """include_class, filter labels to include only these classes (optional)"""
+        """include_class, filter labels to include only these classes (optional)."""
         include_class_array = np.array(include_class).reshape(1, -1)
         for i in range(len(self.labels)):
             if include_class is not None:
@@ -129,7 +129,7 @@ class BaseDataset(Dataset):
                 self.labels[i]['cls'][:, 0] = 0
 
     def load_image(self, i):
-        # Loads 1 image from dataset index 'i', returns (im, resized hw)
+        """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
         im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
         if im is None:  # not cached in RAM
             if fn.exists():  # load npy
@@ -147,7 +147,7 @@ class BaseDataset(Dataset):
         return self.ims[i], self.im_hw0[i], self.im_hw[i]  # im, hw_original, hw_resized
 
     def cache_images(self, cache):
-        # cache images to memory or disk
+        """Cache images to memory or disk."""
         gb = 0  # Gigabytes of cached images
         self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni
         fcn = self.cache_images_to_disk if cache == 'disk' else self.load_image
@@ -164,7 +164,7 @@ class BaseDataset(Dataset):
             pbar.close()
 
     def cache_images_to_disk(self, i):
-        # Saves an image as an *.npy file for faster loading
+        """Saves an image as an *.npy file for faster loading."""
         f = self.npy_files[i]
         if not f.exists():
             np.save(f.as_posix(), cv2.imread(self.im_files[i]))
@@ -211,17 +211,17 @@ class BaseDataset(Dataset):
         return len(self.labels)
 
     def update_labels_info(self, label):
-        """custom your label format here"""
+        """custom your label format here."""
         return label
 
     def build_transforms(self, hyp=None):
         """Users can custom augmentations here
         like:
             if self.augment:
-                # training transforms
+                # Training transforms
                 return Compose([])
             else:
-                # val transforms
+                # Val transforms
                 return Compose([])
         """
         raise NotImplementedError

ultralytics/yolo/data/build.py

@@ -104,7 +104,7 @@ def build_dataloader(cfg, batch, img_path, data_info, stride=32, rect=False, ran
         generator=generator), dataset
 
 
-# build classification
+# Build classification
 # TODO: using cfg like `build_dataloader`
 def build_classification_dataloader(path,
                                     imgsz=224,
@@ -114,7 +114,7 @@ def build_classification_dataloader(path,
                                     rank=-1,
                                     workers=8,
                                     shuffle=True):
-    # Returns Dataloader object to be used with YOLOv5 Classifier
+    """Returns Dataloader object to be used with YOLOv5 Classifier."""
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
     batch_size = min(batch_size, len(dataset))

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -70,7 +70,7 @@ class LoadStreams:
             self.threads[i].start()
         LOGGER.info('')  # newline
 
-        # check for common shapes
+        # Check for common shapes
         s = np.stack([LetterBox(imgsz, auto, stride=stride)(image=x).shape for x in self.imgs])
         self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
         self.auto = auto and self.rect
@@ -81,7 +81,7 @@ class LoadStreams:
             LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
 
     def update(self, i, cap, stream):
-        # Read stream `i` frames in daemon thread
+        """Read stream `i` frames in daemon thread."""
         n, f = 0, self.frames[i]  # frame number, frame array
         while cap.isOpened() and n < f:
             n += 1
@@ -123,7 +123,7 @@ class LoadStreams:
 class LoadScreenshots:
     # YOLOv8 screenshot dataloader, i.e. `yolo predict source=screen`
     def __init__(self, source, imgsz=640, stride=32, auto=True, transforms=None):
-        # source = [screen_number left top width height] (pixels)
+        """source = [screen_number left top width height] (pixels)."""
         check_requirements('mss')
         import mss  # noqa
 
@@ -156,7 +156,7 @@ class LoadScreenshots:
         return self
 
     def __next__(self):
-        # mss screen capture: get raw pixels from the screen as np array
+        """mss screen capture: get raw pixels from the screen as np array."""
         im0 = np.array(self.sct.grab(self.monitor))[:, :, :3]  # [:, :, :3] BGRA to BGR
         s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
 
@@ -256,7 +256,7 @@ class LoadImages:
         return path, im, im0, self.cap, s
 
     def _new_video(self, path):
-        # Create a new video capture object
+        """Create a new video capture object."""
         self.frame = 0
         self.cap = cv2.VideoCapture(path)
         self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
@@ -266,7 +266,7 @@ class LoadImages:
             # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)
 
     def _cv2_rotate(self, im):
-        # Rotate a cv2 video manually
+        """Rotate a cv2 video manually."""
         if self.orientation == 0:
             return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
         elif self.orientation == 180:
@@ -291,7 +291,7 @@ class LoadPilAndNumpy:
         self.auto = auto
         self.transforms = transforms
         self.mode = 'image'
-        # generate fake paths
+        # Generate fake paths
         self.bs = len(self.im0)
 
     @staticmethod

ultralytics/yolo/data/dataloaders/v5augmentations.py

@@ -55,19 +55,19 @@ class Albumentations:
 
 
 def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
-    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
+    """Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std."""
     return TF.normalize(x, mean, std, inplace=inplace)
 
 
 def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
-    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
+    """Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean."""
     for i in range(3):
         x[:, i] = x[:, i] * std[i] + mean[i]
     return x
 
 
 def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
-    # HSV color-space augmentation
+    """HSV color-space augmentation."""
     if hgain or sgain or vgain:
         r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
         hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
@@ -83,7 +83,7 @@ def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
 
 
 def hist_equalize(im, clahe=True, bgr=False):
-    # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
+    """Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255."""
     yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
     if clahe:
         c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
@@ -94,7 +94,7 @@ def hist_equalize(im, clahe=True, bgr=False):
 
 
 def replicate(im, labels):
-    # Replicate labels
+    """Replicate labels."""
     h, w = im.shape[:2]
     boxes = labels[:, 1:].astype(int)
     x1, y1, x2, y2 = boxes.T
@@ -213,7 +213,7 @@ def random_perspective(im,
                 xy = xy @ M.T  # transform
                 xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine
 
-                # clip
+                # Clip
                 new[i] = segment2box(xy, width, height)
 
         else:  # warp boxes
@@ -222,16 +222,16 @@ def random_perspective(im,
             xy = xy @ M.T  # transform
             xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
 
-            # create new boxes
+            # Create new boxes
             x = xy[:, [0, 2, 4, 6]]
             y = xy[:, [1, 3, 5, 7]]
             new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
 
-            # clip
+            # Clip
             new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
             new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
 
-        # filter candidates
+        # Filter candidates
         i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
         targets = targets[i]
         targets[:, 1:5] = new[i]
@@ -240,13 +240,13 @@ def random_perspective(im,
 
 
 def copy_paste(im, labels, segments, p=0.5):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
+    """Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)."""
     n = len(segments)
     if p and n:
         h, w, c = im.shape  # height, width, channels
         im_new = np.zeros(im.shape, np.uint8)
 
-        # calculate ioa first then select indexes randomly
+        # Calculate ioa first then select indexes randomly
         boxes = np.stack([w - labels[:, 3], labels[:, 2], w - labels[:, 1], labels[:, 4]], axis=-1)  # (n, 4)
         ioa = bbox_ioa(boxes, labels[:, 1:5])  # intersection over area
         indexes = np.nonzero((ioa < 0.30).all(1))[0]  # (N, )
@@ -265,7 +265,7 @@ def copy_paste(im, labels, segments, p=0.5):
 
 
 def cutout(im, labels, p=0.5):
-    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
+    """Applies image cutout augmentation https://arxiv.org/abs/1708.04552."""
     if random.random() < p:
         h, w = im.shape[:2]
         scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
@@ -273,16 +273,16 @@ def cutout(im, labels, p=0.5):
             mask_h = random.randint(1, int(h * s))  # create random masks
             mask_w = random.randint(1, int(w * s))
 
-            # box
+            # Box
             xmin = max(0, random.randint(0, w) - mask_w // 2)
             ymin = max(0, random.randint(0, h) - mask_h // 2)
             xmax = min(w, xmin + mask_w)
             ymax = min(h, ymin + mask_h)
 
-            # apply random color mask
+            # Apply random color mask
             im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
 
-            # return unobscured labels
+            # Return unobscured labels
             if len(labels) and s > 0.03:
                 box = np.array([[xmin, ymin, xmax, ymax]], dtype=np.float32)
                 ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h))[0]  # intersection over area
@@ -292,7 +292,7 @@ def cutout(im, labels, p=0.5):
 
 
 def mixup(im, labels, im2, labels2):
-    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
+    """Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf."""
     r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
     im = (im * r + im2 * (1 - r)).astype(np.uint8)
     labels = np.concatenate((labels, labels2), 0)
@@ -350,7 +350,7 @@ def classify_albumentations(
 
 
 def classify_transforms(size=224):
-    # Transforms to apply if albumentations not installed
+    """Transforms to apply if albumentations not installed."""
     assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)'
     # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
     return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])

ultralytics/yolo/data/dataloaders/v5loader.py

@@ -50,7 +50,7 @@ for orientation in ExifTags.TAGS.keys():
 
 
 def get_hash(paths):
-    # Returns a single hash value of a list of paths (files or dirs)
+    """Returns a single hash value of a list of paths (files or dirs)."""
     size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
     h = hashlib.sha256(str(size).encode())  # hash sizes
     h.update(''.join(paths).encode())  # hash paths
@@ -58,7 +58,7 @@ def get_hash(paths):
 
 
 def exif_size(img):
-    # Returns exif-corrected PIL size
+    """Returns exif-corrected PIL size."""
     s = img.size  # (width, height)
     with contextlib.suppress(Exception):
         rotation = dict(img._getexif().items())[orientation]
@@ -94,7 +94,7 @@ def exif_transpose(image):
 
 
 def seed_worker(worker_id):
-    # 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)
@@ -156,7 +156,7 @@ def create_dataloader(path,
 
 
 class InfiniteDataLoader(dataloader.DataLoader):
-    """ Dataloader that reuses workers
+    """Dataloader that reuses workers
 
     Uses same syntax as vanilla DataLoader
     """
@@ -175,7 +175,7 @@ class InfiniteDataLoader(dataloader.DataLoader):
 
 
 class _RepeatSampler:
-    """ Sampler that repeats forever
+    """Sampler that repeats forever
 
     Args:
         sampler (Dataset.sampler): The sampler to repeat.
@@ -192,7 +192,7 @@ class _RepeatSampler:
 class LoadScreenshots:
     # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
     def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
-        # source = [screen_number left top width height] (pixels)
+        """source = [screen_number left top width height] (pixels)."""
         check_requirements('mss')
         import mss
 
@@ -224,7 +224,7 @@ class LoadScreenshots:
         return self
 
     def __next__(self):
-        # mss screen capture: get raw pixels from the screen as np array
+        """mss screen capture: get raw pixels from the screen as np array."""
         im0 = np.array(self.sct.grab(self.monitor))[:, :, :3]  # [:, :, :3] BGRA to BGR
         s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
 
@@ -320,7 +320,7 @@ class LoadImages:
         return path, im, im0, self.cap, s
 
     def _new_video(self, path):
-        # Create a new video capture object
+        """Create a new video capture object."""
         self.frame = 0
         self.cap = cv2.VideoCapture(path)
         self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
@@ -328,7 +328,7 @@ class LoadImages:
         # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)  # disable https://github.com/ultralytics/yolov5/issues/8493
 
     def _cv2_rotate(self, im):
-        # Rotate a cv2 video manually
+        """Rotate a cv2 video manually."""
         if self.orientation == 0:
             return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
         elif self.orientation == 180:
@@ -379,7 +379,7 @@ class LoadStreams:
             self.threads[i].start()
         LOGGER.info('')  # newline
 
-        # check for common shapes
+        # Check for common shapes
         s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs])
         self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
         self.auto = auto and self.rect
@@ -388,7 +388,7 @@ class LoadStreams:
             LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
 
     def update(self, i, cap, stream):
-        # Read stream `i` frames in daemon thread
+        """Read stream `i` frames in daemon thread."""
         n, f = 0, self.frames[i]  # frame number, frame array
         while cap.isOpened() and n < f:
             n += 1
@@ -428,13 +428,13 @@ class LoadStreams:
 
 
 def img2label_paths(img_paths):
-    # Define label paths as a function of image paths
+    """Define label paths as a function of image paths."""
     sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}'  # /images/, /labels/ substrings
     return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]
 
 
 class LoadImagesAndLabels(Dataset):
-    # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
+    """YOLOv5 train_loader/val_loader, loads images and labels for training and validation."""
     cache_version = 0.6  # dataset labels *.cache version
     rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
 
@@ -590,7 +590,7 @@ class LoadImagesAndLabels(Dataset):
                 pbar.close()
 
     def check_cache_ram(self, safety_margin=0.1, prefix=''):
-        # Check image caching requirements vs available memory
+        """Check image caching requirements vs available memory."""
         b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
         n = min(self.n, 30)  # extrapolate from 30 random images
         for _ in range(n):
@@ -648,12 +648,6 @@ class LoadImagesAndLabels(Dataset):
     def __len__(self):
         return len(self.im_files)
 
-    # def __iter__(self):
-    #     self.count = -1
-    #     print('ran dataset iter')
-    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
-    #     return self
-
     def __getitem__(self, index):
         index = self.indices[index]  # linear, shuffled, or image_weights
 
@@ -729,7 +723,7 @@ class LoadImagesAndLabels(Dataset):
         return torch.from_numpy(img), labels_out, self.im_files[index], shapes
 
     def load_image(self, i):
-        # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)
+        """Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)."""
         im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i],
         if im is None:  # not cached in RAM
             if fn.exists():  # load npy
@@ -746,13 +740,13 @@ class LoadImagesAndLabels(Dataset):
         return self.ims[i], self.im_hw0[i], self.im_hw[i]  # im, hw_original, hw_resized
 
     def cache_images_to_disk(self, i):
-        # Saves an image as an *.npy file for faster loading
+        """Saves an image as an *.npy file for faster loading."""
         f = self.npy_files[i]
         if not f.exists():
             np.save(f.as_posix(), cv2.imread(self.im_files[i]))
 
     def load_mosaic(self, index):
-        # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
+        """YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic."""
         labels4, segments4 = [], []
         s = self.img_size
         yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border)  # mosaic center x, y
@@ -762,7 +756,7 @@ class LoadImagesAndLabels(Dataset):
             # Load image
             img, _, (h, w) = self.load_image(index)
 
-            # place img in img4
+            # Place img in img4
             if i == 0:  # top left
                 img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
                 x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
@@ -810,7 +804,7 @@ class LoadImagesAndLabels(Dataset):
         return img4, labels4
 
     def load_mosaic9(self, index):
-        # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic
+        """YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic."""
         labels9, segments9 = [], []
         s = self.img_size
         indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
@@ -820,7 +814,7 @@ class LoadImagesAndLabels(Dataset):
             # Load image
             img, _, (h, w) = self.load_image(index)
 
-            # place img in img9
+            # Place img in img9
             if i == 0:  # center
                 img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
                 h0, w0 = h, w
@@ -888,7 +882,7 @@ class LoadImagesAndLabels(Dataset):
 
     @staticmethod
     def collate_fn(batch):
-        # YOLOv8 collate function, outputs dict
+        """YOLOv8 collate function, outputs dict."""
         im, label, path, shapes = zip(*batch)  # transposed
         for i, lb in enumerate(label):
             lb[:, 0] = i  # add target image index for build_targets()
@@ -904,7 +898,7 @@ class LoadImagesAndLabels(Dataset):
 
     @staticmethod
     def collate_fn_old(batch):
-        # YOLOv5 original collate function
+        """YOLOv5 original collate function."""
         im, label, path, shapes = zip(*batch)  # transposed
         for i, lb in enumerate(label):
             lb[:, 0] = i  # add target image index for build_targets()
@@ -913,7 +907,7 @@ class LoadImagesAndLabels(Dataset):
 
 # Ancillary functions --------------------------------------------------------------------------------------------------
 def flatten_recursive(path=DATASETS_DIR / 'coco128'):
-    # Flatten a recursive directory by bringing all files to top level
+    """Flatten a recursive directory by bringing all files to top level."""
     new_path = Path(f'{str(path)}_flat')
     if os.path.exists(new_path):
         shutil.rmtree(new_path)  # delete output folder
@@ -930,11 +924,11 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders impo
     n = len(files)  # number of files
     for im_file in tqdm(files, total=n):
         if im_file.suffix[1:] in IMG_FORMATS:
-            # image
+            # Image
             im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB
             h, w = im.shape[:2]
 
-            # labels
+            # Labels
             lb_file = Path(img2label_paths([str(im_file)])[0])
             if Path(lb_file).exists():
                 with open(lb_file) as f:
@@ -947,7 +941,7 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders impo
                         f.parent.mkdir(parents=True)
 
                     b = x[1:] * [w, h, w, h]  # box
-                    # b[2:] = b[2:].max()  # rectangle to square
+                    # B[2:] = b[2:].max()  # rectangle to square
                     b[2:] = b[2:] * 1.2 + 3  # pad
                     b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)
 
@@ -957,7 +951,7 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders impo
 
 
 def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
-    """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
+    """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
     Usage: from utils.dataloaders import *; autosplit()
     Arguments
         path:            Path to images directory
@@ -983,11 +977,11 @@ def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), ann
 
 
 def verify_image_label(args):
-    # Verify one image-label pair
+    """Verify one image-label pair."""
     im_file, lb_file, prefix = args
     nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', []  # number (missing, found, empty, corrupt), message, segments
     try:
-        # verify images
+        # Verify images
         im = Image.open(im_file)
         im.verify()  # PIL verify
         shape = exif_size(im)  # image size
@@ -1000,7 +994,7 @@ def verify_image_label(args):
                     ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
                     msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
 
-        # verify labels
+        # Verify labels
         if os.path.isfile(lb_file):
             nf = 1  # label found
             with open(lb_file) as f:
@@ -1077,7 +1071,7 @@ def create_classification_dataloader(path,
                                      rank=-1,
                                      workers=8,
                                      shuffle=True):
-    # Returns Dataloader object to be used with YOLOv5 Classifier
+    """Returns Dataloader object to be used with YOLOv5 Classifier."""
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
     batch_size = min(batch_size, len(dataset))

ultralytics/yolo/data/dataset.py

@@ -193,7 +193,7 @@ class YOLODataset(BaseDataset):
         self.transforms = self.build_transforms(hyp)
 
     def update_labels_info(self, label):
-        """custom your label format here"""
+        """custom your label format here."""
         # NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label
         # we can make it also support classification and semantic segmentation by add or remove some dict keys there.
         bboxes = label.pop('bboxes')

ultralytics/yolo/data/dataset_wrappers.py

@@ -39,7 +39,7 @@ class MixAndRectDataset:
         """
         labels = deepcopy(self.dataset[index])
         for transform in self.dataset.transforms.tolist():
-            # mosaic and mixup
+            # Mosaic and mixup
             if hasattr(transform, 'get_indexes'):
                 indexes = transform.get_indexes(self.dataset)
                 if not isinstance(indexes, collections.abc.Sequence):

ultralytics/yolo/data/utils.py

@@ -37,13 +37,13 @@ for orientation in ExifTags.TAGS.keys():
 
 
 def img2label_paths(img_paths):
-    # Define label paths as a function of image paths
+    """Define label paths as a function of image paths."""
     sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}'  # /images/, /labels/ substrings
     return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]
 
 
 def get_hash(paths):
-    # Returns a single hash value of a list of paths (files or dirs)
+    """Returns a single hash value of a list of paths (files or dirs)."""
     size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
     h = hashlib.sha256(str(size).encode())  # hash sizes
     h.update(''.join(paths).encode())  # hash paths
@@ -51,7 +51,7 @@ def get_hash(paths):
 
 
 def exif_size(img):
-    # Returns exif-corrected PIL size
+    """Returns exif-corrected PIL size."""
     s = img.size  # (width, height)
     with contextlib.suppress(Exception):
         rotation = dict(img._getexif().items())[orientation]
@@ -61,12 +61,12 @@ def exif_size(img):
 
 
 def verify_image_label(args):
-    # Verify one image-label pair
+    """Verify one image-label pair."""
     im_file, lb_file, prefix, keypoint, num_cls, nkpt, ndim = args
-    # number (missing, found, empty, corrupt), message, segments, keypoints
+    # Number (missing, found, empty, corrupt), message, segments, keypoints
     nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, '', [], None
     try:
-        # verify images
+        # Verify images
         im = Image.open(im_file)
         im.verify()  # PIL verify
         shape = exif_size(im)  # image size
@@ -80,7 +80,7 @@ def verify_image_label(args):
                     ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
                     msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
 
-        # verify labels
+        # Verify labels
         if os.path.isfile(lb_file):
             nf = 1  # label found
             with open(lb_file) as f:
@@ -191,7 +191,7 @@ def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
 
 
 def check_det_dataset(dataset, autodownload=True):
-    # Download, check and/or unzip dataset if not found locally
+    """Download, check and/or unzip dataset if not found locally."""
     data = check_file(dataset)
 
     # Download (optional)
@@ -321,7 +321,7 @@ class HUBDatasetStats():
     """
 
     def __init__(self, path='coco128.yaml', autodownload=False):
-        # Initialize class
+        """Initialize class."""
         zipped, data_dir, yaml_path = self._unzip(Path(path))
         try:
             # data = yaml_load(check_yaml(yaml_path))  # data dict
@@ -339,7 +339,7 @@ class HUBDatasetStats():
 
     @staticmethod
     def _find_yaml(dir):
-        # Return data.yaml file
+        """Return data.yaml file."""
         files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml'))  # try root level first and then recursive
         assert files, f'No *.yaml file found in {dir}'
         if len(files) > 1:
@@ -349,7 +349,7 @@ class HUBDatasetStats():
         return files[0]
 
     def _unzip(self, path):
-        # Unzip data.zip
+        """Unzip data.zip."""
         if not str(path).endswith('.zip'):  # path is data.yaml
             return False, None, path
         assert Path(path).is_file(), f'Error unzipping {path}, file not found'
@@ -362,12 +362,12 @@ class HUBDatasetStats():
         compress_one_image(f, self.im_dir / Path(f).name)  # save to dataset-hub
 
     def get_json(self, save=False, verbose=False):
-        # Return dataset JSON for Ultralytics HUB
+        """Return dataset JSON for Ultralytics HUB."""
         # from ultralytics.yolo.data import YOLODataset
         from ultralytics.yolo.data.dataloaders.v5loader import LoadImagesAndLabels
 
         def _round(labels):
-            # Update labels to integer class and 6 decimal place floats
+            """Update labels to integer class and 6 decimal place floats."""
             return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]
 
         for split in 'train', 'val', 'test':
@@ -400,7 +400,7 @@ class HUBDatasetStats():
         return self.stats
 
     def process_images(self):
-        # Compress images for Ultralytics HUB
+        """Compress images for Ultralytics HUB."""
         # from ultralytics.yolo.data import YOLODataset
         from ultralytics.yolo.data.dataloaders.v5loader import LoadImagesAndLabels
 

ultralytics/yolo/engine/exporter.py

@@ -73,7 +73,7 @@ ARM64 = platform.machine() in ('arm64', 'aarch64')
 
 
 def export_formats():
-    """YOLOv8 export formats"""
+    """YOLOv8 export formats."""
     import pandas
     x = [
         ['PyTorch', '-', '.pt', True, True],
@@ -92,7 +92,7 @@ def export_formats():
 
 
 def gd_outputs(gd):
-    """TensorFlow GraphDef model output node names"""
+    """TensorFlow GraphDef model output node names."""
     name_list, input_list = [], []
     for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
         name_list.append(node.name)
@@ -101,7 +101,7 @@ def gd_outputs(gd):
 
 
 def try_export(inner_func):
-    """YOLOv8 export decorator, i..e @try_export"""
+    """YOLOv8 export decorator, i..e @try_export."""
     inner_args = get_default_args(inner_func)
 
     def outer_func(*args, **kwargs):
@@ -119,7 +119,7 @@ def try_export(inner_func):
 
 
 class iOSDetectModel(torch.nn.Module):
-    """Wrap an Ultralytics YOLO model for iOS export"""
+    """Wrap an Ultralytics YOLO model for iOS export."""
 
     def __init__(self, model, im):
         super().__init__()
@@ -246,28 +246,28 @@ class Exporter:
         # Exports
         f = [''] * len(fmts)  # exported filenames
         if jit:  # TorchScript
-            f[0], _ = self._export_torchscript()
+            f[0], _ = self.export_torchscript()
         if engine:  # TensorRT required before ONNX
-            f[1], _ = self._export_engine()
+            f[1], _ = self.export_engine()
         if onnx or xml:  # OpenVINO requires ONNX
-            f[2], _ = self._export_onnx()
+            f[2], _ = self.export_onnx()
         if xml:  # OpenVINO
-            f[3], _ = self._export_openvino()
+            f[3], _ = self.export_openvino()
         if coreml:  # CoreML
-            f[4], _ = self._export_coreml()
+            f[4], _ = self.export_coreml()
         if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
             self.args.int8 |= edgetpu
-            f[5], s_model = self._export_saved_model()
+            f[5], s_model = self.export_saved_model()
             if pb or tfjs:  # pb prerequisite to tfjs
-                f[6], _ = self._export_pb(s_model)
+                f[6], _ = self.export_pb(s_model)
             if tflite:
-                f[7], _ = self._export_tflite(s_model, nms=False, agnostic_nms=self.args.agnostic_nms)
+                f[7], _ = self.export_tflite(s_model, nms=False, agnostic_nms=self.args.agnostic_nms)
             if edgetpu:
-                f[8], _ = self._export_edgetpu(tflite_model=Path(f[5]) / f'{self.file.stem}_full_integer_quant.tflite')
+                f[8], _ = self.export_edgetpu(tflite_model=Path(f[5]) / f'{self.file.stem}_full_integer_quant.tflite')
             if tfjs:
-                f[9], _ = self._export_tfjs()
+                f[9], _ = self.export_tfjs()
         if paddle:  # PaddlePaddle
-            f[10], _ = self._export_paddle()
+            f[10], _ = self.export_paddle()
 
         # Finish
         f = [str(x) for x in f if x]  # filter out '' and None
@@ -289,8 +289,8 @@ class Exporter:
         return f  # return list of exported files/dirs
 
     @try_export
-    def _export_torchscript(self, prefix=colorstr('TorchScript:')):
+    def export_torchscript(self, prefix=colorstr('TorchScript:')):
-        # YOLOv8 TorchScript model export
+        """YOLOv8 TorchScript model export."""
         LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
         f = self.file.with_suffix('.torchscript')
 
@@ -305,8 +305,8 @@ class Exporter:
         return f, None
 
     @try_export
-    def _export_onnx(self, prefix=colorstr('ONNX:')):
+    def export_onnx(self, prefix=colorstr('ONNX:')):
-        # YOLOv8 ONNX export
+        """YOLOv8 ONNX export."""
         requirements = ['onnx>=1.12.0']
         if self.args.simplify:
             requirements += ['onnxsim>=0.4.17', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime']
@@ -363,8 +363,8 @@ class Exporter:
         return f, model_onnx
 
     @try_export
-    def _export_openvino(self, prefix=colorstr('OpenVINO:')):
+    def export_openvino(self, prefix=colorstr('OpenVINO:')):
-        # YOLOv8 OpenVINO export
+        """YOLOv8 OpenVINO export."""
         check_requirements('openvino-dev>=2022.3')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
         import openvino.runtime as ov  # noqa
         from openvino.tools import mo  # noqa
@@ -383,8 +383,8 @@ class Exporter:
         return f, None
 
     @try_export
-    def _export_paddle(self, prefix=colorstr('PaddlePaddle:')):
+    def export_paddle(self, prefix=colorstr('PaddlePaddle:')):
-        # YOLOv8 Paddle export
+        """YOLOv8 Paddle export."""
         check_requirements(('paddlepaddle', 'x2paddle'))
         import x2paddle  # noqa
         from x2paddle.convert import pytorch2paddle  # noqa
@@ -397,8 +397,8 @@ class Exporter:
         return f, None
 
     @try_export
-    def _export_coreml(self, prefix=colorstr('CoreML:')):
+    def export_coreml(self, prefix=colorstr('CoreML:')):
-        # YOLOv8 CoreML export
+        """YOLOv8 CoreML export."""
         check_requirements('coremltools>=6.0')
         import coremltools as ct  # noqa
 
@@ -439,8 +439,8 @@ class Exporter:
         return f, ct_model
 
     @try_export
-    def _export_engine(self, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
+    def export_engine(self, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
-        # YOLOv8 TensorRT export https://developer.nvidia.com/tensorrt
+        """YOLOv8 TensorRT export https://developer.nvidia.com/tensorrt."""
         assert self.im.device.type != 'cpu', "export running on CPU but must be on GPU, i.e. use 'device=0'"
         try:
             import tensorrt as trt  # noqa
@@ -451,7 +451,7 @@ class Exporter:
 
         check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=8.0.0
         self.args.simplify = True
-        f_onnx, _ = self._export_onnx()
+        f_onnx, _ = self.export_onnx()
 
         LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
         assert Path(f_onnx).exists(), f'failed to export ONNX file: {f_onnx}'
@@ -504,9 +504,8 @@ class Exporter:
         return f, None
 
     @try_export
-    def _export_saved_model(self, prefix=colorstr('TensorFlow SavedModel:')):
+    def export_saved_model(self, prefix=colorstr('TensorFlow SavedModel:')):
-
+        """YOLOv8 TensorFlow SavedModel export."""
-        # YOLOv8 TensorFlow SavedModel export
         try:
             import tensorflow as tf  # noqa
         except ImportError:
@@ -525,7 +524,7 @@ class Exporter:
 
         # Export to ONNX
         self.args.simplify = True
-        f_onnx, _ = self._export_onnx()
+        f_onnx, _ = self.export_onnx()
 
         # Export to TF
         int8 = '-oiqt -qt per-tensor' if self.args.int8 else ''
@@ -551,8 +550,8 @@ class Exporter:
         return str(f), keras_model
 
     @try_export
-    def _export_pb(self, keras_model, prefix=colorstr('TensorFlow GraphDef:')):
+    def export_pb(self, keras_model, prefix=colorstr('TensorFlow GraphDef:')):
-        # YOLOv8 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
+        """YOLOv8 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow."""
         import tensorflow as tf  # noqa
         from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2  # noqa
 
@@ -567,8 +566,8 @@ class Exporter:
         return f, None
 
     @try_export
-    def _export_tflite(self, keras_model, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
+    def export_tflite(self, keras_model, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
-        # YOLOv8 TensorFlow Lite export
+        """YOLOv8 TensorFlow Lite export."""
         import tensorflow as tf  # noqa
 
         LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
@@ -581,44 +580,9 @@ class Exporter:
             f = saved_model / f'{self.file.stem}_float32.tflite'
         return str(f), None
 
-        # # OLD TFLITE EXPORT CODE BELOW -------------------------------------------------------------------------------
-        # batch_size, ch, *imgsz = list(self.im.shape)  # BCHW
-        # f = str(self.file).replace(self.file.suffix, '-fp16.tflite')
-        #
-        # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
-        # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
-        # converter.target_spec.supported_types = [tf.float16]
-        # converter.optimizations = [tf.lite.Optimize.DEFAULT]
-        # if self.args.int8:
-        #
-        #     def representative_dataset_gen(dataset, n_images=100):
-        #         # Dataset generator for use with converter.representative_dataset, returns a generator of np arrays
-        #         for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
-        #             im = np.transpose(img, [1, 2, 0])
-        #             im = np.expand_dims(im, axis=0).astype(np.float32)
-        #             im /= 255
-        #             yield [im]
-        #             if n >= n_images:
-        #                 break
-        #
-        #     dataset = LoadImages(check_det_dataset(self.args.data)['train'], imgsz=imgsz, auto=False)
-        #     converter.representative_dataset = lambda: representative_dataset_gen(dataset, n_images=100)
-        #     converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
-        #     converter.target_spec.supported_types = []
-        #     converter.inference_input_type = tf.uint8  # or tf.int8
-        #     converter.inference_output_type = tf.uint8  # or tf.int8
-        #     converter.experimental_new_quantizer = True
-        #     f = str(self.file).replace(self.file.suffix, '-int8.tflite')
-        # if nms or agnostic_nms:
-        #     converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
-        #
-        # tflite_model = converter.convert()
-        # open(f, 'wb').write(tflite_model)
-        # return f, None
-
     @try_export
-    def _export_edgetpu(self, tflite_model='', prefix=colorstr('Edge TPU:')):
+    def export_edgetpu(self, tflite_model='', prefix=colorstr('Edge TPU:')):
-        # YOLOv8 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
+        """YOLOv8 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/."""
         LOGGER.warning(f'{prefix} WARNING ⚠️ Edge TPU known bug https://github.com/ultralytics/ultralytics/issues/1185')
 
         cmd = 'edgetpu_compiler --version'
@@ -644,8 +608,8 @@ class Exporter:
         return f, None
 
     @try_export
-    def _export_tfjs(self, prefix=colorstr('TensorFlow.js:')):
+    def export_tfjs(self, prefix=colorstr('TensorFlow.js:')):
-        # YOLOv8 TensorFlow.js export
+        """YOLOv8 TensorFlow.js export."""
         check_requirements('tensorflowjs')
         import tensorflow as tf
         import tensorflowjs as tfjs  # noqa
@@ -681,7 +645,7 @@ class Exporter:
         return f, None
 
     def _add_tflite_metadata(self, file):
-        # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
+        """Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata."""
         from tflite_support import flatbuffers  # noqa
         from tflite_support import metadata as _metadata  # noqa
         from tflite_support import metadata_schema_py_generated as _metadata_fb  # noqa

ultralytics/yolo/engine/model.py

@@ -35,6 +35,7 @@ class YOLO:
 
     Args:
         model (str, Path): Path to the model file to load or create.
+        task (Any, optional): Task type for the YOLO model. Defaults to None.
 
     Attributes:
         predictor (Any): The predictor object.
@@ -76,7 +77,6 @@ class YOLO:
         Args:
             model (Union[str, Path], optional): Path or name of the model to load or create. Defaults to 'yolov8n.pt'.
             task (Any, optional): Task type for the YOLO model. Defaults to None.
-
         """
         self.callbacks = callbacks.get_default_callbacks()
         self.predictor = None  # reuse predictor
@@ -273,7 +273,7 @@ class YOLO:
     @smart_inference_mode()
     def val(self, data=None, **kwargs):
         """
-        Validate a model on a given dataset .
+        Validate a model on a given dataset.
 
         Args:
             data (str): The dataset to validate on. Accepts all formats accepted by yolo
@@ -365,7 +365,7 @@ class YOLO:
             self.model = self.trainer.model
         self.trainer.hub_session = self.session  # attach optional HUB session
         self.trainer.train()
-        # update model and cfg after training
+        # Update model and cfg after training
         if RANK in (-1, 0):
             self.model, _ = attempt_load_one_weight(str(self.trainer.best))
             self.overrides = self.model.args
@@ -467,7 +467,7 @@ class YOLO:
     @property
     def names(self):
         """
-         Returns class names of the loaded model.
+        Returns class names of the loaded model.
         """
         return self.model.names if hasattr(self.model, 'names') else None
 
@@ -481,7 +481,7 @@ class YOLO:
     @property
     def transforms(self):
         """
-         Returns transform of the loaded model.
+        Returns transform of the loaded model.
         """
         return self.model.transforms if hasattr(self.model, 'transforms') else None
 

ultralytics/yolo/engine/predictor.py

@@ -134,7 +134,7 @@ class BasePredictor:
             if not self.args.retina_masks:
                 plot_args['im_gpu'] = im[idx]
             self.plotted_img = result.plot(**plot_args)
-        # write
+        # Write
         if self.args.save_txt:
             result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf)
         if self.args.save_crop:
@@ -153,7 +153,7 @@ class BasePredictor:
             return list(self.stream_inference(source, model))  # merge list of Result into one
 
     def predict_cli(self, source=None, model=None):
-        # Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
+        """Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode."""
         gen = self.stream_inference(source, model)
         for _ in gen:  # running CLI inference without accumulating any outputs (do not modify)
             pass
@@ -182,16 +182,16 @@ class BasePredictor:
         if self.args.verbose:
             LOGGER.info('')
 
-        # setup model
+        # Setup model
         if not self.model:
             self.setup_model(model)
-        # setup source every time predict is called
+        # Setup source every time predict is called
         self.setup_source(source if source is not None else self.args.source)
 
-        # check if save_dir/ label file exists
+        # Check if save_dir/ label file exists
         if self.args.save or self.args.save_txt:
             (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
-        # warmup model
+        # Warmup model
         if not self.done_warmup:
             self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz))
             self.done_warmup = True
@@ -204,22 +204,22 @@ class BasePredictor:
             path, im, im0s, vid_cap, s = batch
             visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
 
-            # preprocess
+            # Preprocess
             with self.dt[0]:
                 im = self.preprocess(im)
                 if len(im.shape) == 3:
                     im = im[None]  # expand for batch dim
 
-            # inference
+            # Inference
             with self.dt[1]:
                 preds = self.model(im, augment=self.args.augment, visualize=visualize)
 
-            # postprocess
+            # Postprocess
             with self.dt[2]:
                 self.results = self.postprocess(preds, im, im0s)
             self.run_callbacks('on_predict_postprocess_end')
 
-            # visualize, save, write results
+            # Visualize, save, write results
             n = len(im)
             for i in range(n):
                 self.results[i].speed = {
@@ -288,7 +288,7 @@ class BasePredictor:
 
     def save_preds(self, vid_cap, idx, save_path):
         im0 = self.plotted_img
-        # save imgs
+        # Save imgs
         if self.dataset.mode == 'image':
             cv2.imwrite(save_path, im0)
         else:  # 'video' or 'stream'

ultralytics/yolo/engine/results.py

@@ -262,12 +262,12 @@ class Results(SimpleClass):
         kpts = self.keypoints
         texts = []
         if probs is not None:
-            # classify
+            # Classify
             n5 = min(len(self.names), 5)
             top5i = probs.argsort(0, descending=True)[:n5].tolist()  # top 5 indices
             [texts.append(f'{probs[j]:.2f} {self.names[j]}') for j in top5i]
         elif boxes:
-            # detect/segment/pose
+            # Detect/segment/pose
             for j, d in enumerate(boxes):
                 c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
                 line = (c, *d.xywhn.view(-1))
@@ -418,7 +418,7 @@ class Masks(BaseTensor):
     @property
     @lru_cache(maxsize=1)
     def segments(self):
-        # Segments-deprecated (normalized)
+        """Segments-deprecated (normalized)."""
         LOGGER.warning("WARNING ⚠️ 'Masks.segments' is deprecated. Use 'Masks.xyn' for segments (normalized) and "
                        "'Masks.xy' for segments (pixels) instead.")
         return self.xyn
@@ -426,7 +426,7 @@ class Masks(BaseTensor):
     @property
     @lru_cache(maxsize=1)
     def xyn(self):
-        # Segments (normalized)
+        """Segments (normalized)."""
         return [
             ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=True)
             for x in ops.masks2segments(self.data)]
@@ -434,7 +434,7 @@ class Masks(BaseTensor):
     @property
     @lru_cache(maxsize=1)
     def xy(self):
-        # Segments (pixels)
+        """Segments (pixels)."""
         return [
             ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=False)
             for x in ops.masks2segments(self.data)]

ultralytics/yolo/engine/trainer.py

@@ -163,7 +163,7 @@ class BaseTrainer:
             callback(self)
 
     def train(self):
-        # Allow device='', device=None on Multi-GPU systems to default to device=0
+        """Allow device='', device=None on Multi-GPU systems to default to device=0."""
         if isinstance(self.args.device, int) or self.args.device:  # i.e. device=0 or device=[0,1,2,3]
             world_size = torch.cuda.device_count()
         elif torch.cuda.is_available():  # i.e. device=None or device=''
@@ -306,7 +306,7 @@ class BaseTrainer:
                     xi = [0, nw]  # x interp
                     self.accumulate = max(1, np.interp(ni, xi, [1, self.args.nbs / self.batch_size]).round())
                     for j, x in enumerate(self.optimizer.param_groups):
-                        # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
+                        # Bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
                         x['lr'] = np.interp(
                             ni, xi, [self.args.warmup_bias_lr if j == 0 else 0.0, x['initial_lr'] * self.lf(epoch)])
                         if 'momentum' in x:
@@ -631,7 +631,7 @@ def check_amp(model):
         return False  # AMP only used on CUDA devices
 
     def amp_allclose(m, im):
-        # All close FP32 vs AMP results
+        """All close FP32 vs AMP results."""
         a = m(im, device=device, verbose=False)[0].boxes.data  # FP32 inference
         with torch.cuda.amp.autocast(True):
             b = m(im, device=device, verbose=False)[0].boxes.data  # AMP inference

ultralytics/yolo/engine/validator.py

@@ -149,20 +149,20 @@ class BaseValidator:
         for batch_i, batch in enumerate(bar):
             self.run_callbacks('on_val_batch_start')
             self.batch_i = batch_i
-            # preprocess
+            # Preprocess
             with dt[0]:
                 batch = self.preprocess(batch)
 
-            # inference
+            # Inference
             with dt[1]:
                 preds = model(batch['img'])
 
-            # loss
+            # Loss
             with dt[2]:
                 if self.training:
                     self.loss += trainer.criterion(preds, batch)[1]
 
-            # postprocess
+            # Postprocess
             with dt[3]:
                 preds = self.postprocess(preds)
 

ultralytics/yolo/utils/__init__.py

@@ -199,7 +199,7 @@ def plt_settings(rcparams={'font.size': 11}, backend='Agg'):
 
 
 def set_logging(name=LOGGING_NAME, verbose=True):
-    # sets up logging for the given name
+    """Sets up logging for the given name."""
     rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings
     level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
     logging.config.dictConfig({
@@ -539,12 +539,12 @@ SETTINGS_YAML = USER_CONFIG_DIR / 'settings.yaml'
 
 
 def emojis(string=''):
-    # Return platform-dependent emoji-safe version of string
+    """Return platform-dependent emoji-safe version of string."""
     return string.encode().decode('ascii', 'ignore') if WINDOWS else string
 
 
 def colorstr(*input):
-    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
+    """Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')."""
     *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
     colors = {
         'black': '\033[30m',  # basic colors
@@ -570,7 +570,8 @@ def colorstr(*input):
 
 
 class TryExcept(contextlib.ContextDecorator):
-    # YOLOv8 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
+    """YOLOv8 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager."""
+
     def __init__(self, msg='', verbose=True):
         self.msg = msg
         self.verbose = verbose
@@ -585,7 +586,8 @@ class TryExcept(contextlib.ContextDecorator):
 
 
 def threaded(func):
-    # Multi-threads a target function and returns thread. Usage: @threaded decorator
+    """Multi-threads a target function and returns thread. Usage: @threaded decorator."""
+
     def wrapper(*args, **kwargs):
         thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
         thread.start()
@@ -703,13 +705,13 @@ def deprecation_warn(arg, new_arg, version=None):
 
 
 def clean_url(url):
-    # Strip auth from URL, i.e. https://url.com/file.txt?auth -> https://url.com/file.txt
+    """Strip auth from URL, i.e. https://url.com/file.txt?auth -> https://url.com/file.txt."""
     url = str(Path(url)).replace(':/', '://')  # Pathlib turns :// -> :/
     return urllib.parse.unquote(url).split('?')[0]  # '%2F' to '/', split https://url.com/file.txt?auth
 
 
 def url2file(url):
-    # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
+    """Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt."""
     return Path(clean_url(url)).name
 
 

ultralytics/yolo/utils/callbacks/comet.py

@@ -15,20 +15,20 @@ except (ImportError, AssertionError):
 
 COMET_MODE = os.getenv('COMET_MODE', 'online')
 COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'YOLOv8')
-# determines how many batches of image predictions to log from the validation set
+# Determines how many batches of image predictions to log from the validation set
 COMET_EVAL_BATCH_LOGGING_INTERVAL = int(os.getenv('COMET_EVAL_BATCH_LOGGING_INTERVAL', 1))
-# determines whether to log confusion matrix every evaluation epoch
+# Determines whether to log confusion matrix every evaluation epoch
 COMET_EVAL_LOG_CONFUSION_MATRIX = (os.getenv('COMET_EVAL_LOG_CONFUSION_MATRIX', 'true').lower() == 'true')
-# determines whether to log image predictions every evaluation epoch
+# Determines whether to log image predictions every evaluation epoch
 COMET_EVAL_LOG_IMAGE_PREDICTIONS = (os.getenv('COMET_EVAL_LOG_IMAGE_PREDICTIONS', 'true').lower() == 'true')
 COMET_MAX_IMAGE_PREDICTIONS = int(os.getenv('COMET_MAX_IMAGE_PREDICTIONS', 100))
 
-# ensures certain logging functions only run for supported tasks
+# Ensures certain logging functions only run for supported tasks
 COMET_SUPPORTED_TASKS = ['detect']
-# scales reported confidence scores (0.0-1.0) by this value
+# Scales reported confidence scores (0.0-1.0) by this value
 COMET_MAX_CONFIDENCE_SCORE = int(os.getenv('COMET_MAX_CONFIDENCE_SCORE', 100))
 
-# names of plots created by YOLOv8 that are logged to Comet
+# Names of plots created by YOLOv8 that are logged to Comet
 EVALUATION_PLOT_NAMES = 'F1_curve', 'P_curve', 'R_curve', 'PR_curve', 'confusion_matrix'
 LABEL_PLOT_NAMES = 'labels', 'labels_correlogram'
 
@@ -43,7 +43,7 @@ def _get_experiment_type(mode, project_name):
 
 
 def _create_experiment(args):
-    # Ensures that the experiment object is only created in a single process during distributed training.
+    """Ensures that the experiment object is only created in a single process during distributed training."""
     if RANK not in (-1, 0):
         return
     try:
@@ -83,13 +83,13 @@ def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, origin
 
     resized_image_height, resized_image_width = resized_image_shape
 
-    # convert normalized xywh format predictions to xyxy in resized scale format
+    # Convert normalized xywh format predictions to xyxy in resized scale format
     box = ops.xywhn2xyxy(box, h=resized_image_height, w=resized_image_width)
-    # scale box predictions from resized image scale back to original image scale
+    # Scale box predictions from resized image scale back to original image scale
     box = ops.scale_boxes(resized_image_shape, box, original_image_shape, ratio_pad)
     # Convert bounding box format from xyxy to xywh for Comet logging
     box = ops.xyxy2xywh(box)
-    # adjust xy center to correspond top-left corner
+    # Adjust xy center to correspond top-left corner
     box[:2] -= box[2:] / 2
     box = box.tolist()
 

ultralytics/yolo/utils/checks.py

@@ -244,7 +244,7 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
 
 
 def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
-    # Check file(s) for acceptable suffix
+    """Check file(s) for acceptable suffix."""
     if file and suffix:
         if isinstance(suffix, str):
             suffix = (suffix, )
@@ -255,7 +255,7 @@ def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
 
 
 def check_yolov5u_filename(file: str, verbose: bool = True):
-    # Replace legacy YOLOv5 filenames with updated YOLOv5u filenames
+    """Replace legacy YOLOv5 filenames with updated YOLOv5u filenames."""
     if ('yolov3' in file or 'yolov5' in file) and 'u' not in file:
         original_file = file
         file = re.sub(r'(.*yolov5([nsmlx]))\.pt', '\\1u.pt', file)  # i.e. yolov5n.pt -> yolov5nu.pt
@@ -269,7 +269,7 @@ def check_yolov5u_filename(file: str, verbose: bool = True):
 
 
 def check_file(file, suffix='', download=True, hard=True):
-    # Search/download file (if necessary) and return path
+    """Search/download file (if necessary) and return path."""
     check_suffix(file, suffix)  # optional
     file = str(file).strip()  # convert to string and strip spaces
     file = check_yolov5u_filename(file)  # yolov5n -> yolov5nu
@@ -300,7 +300,7 @@ def check_yaml(file, suffix=('.yaml', '.yml'), hard=True):
 
 
 def check_imshow(warn=False):
-    # Check if environment supports image displays
+    """Check if environment supports image displays."""
     try:
         assert not any((is_colab(), is_kaggle(), is_docker()))
         cv2.imshow('test', np.zeros((1, 1, 3)))
@@ -346,9 +346,10 @@ def git_describe(path=ROOT):  # path must be a directory
 
 
 def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
-    # Print function arguments (optional args dict)
+    """Print function arguments (optional args dict)."""
+
     def strip_auth(v):
-        # Clean longer Ultralytics HUB URLs by stripping potential authentication information
+        """Clean longer Ultralytics HUB URLs by stripping potential authentication information."""
         return clean_url(v) if (isinstance(v, str) and v.startswith('http') and len(v) > 100) else v
 
     x = inspect.currentframe().f_back  # previous frame

ultralytics/yolo/utils/dist.py

@@ -59,6 +59,6 @@ def generate_ddp_command(world_size, trainer):
 
 
 def ddp_cleanup(trainer, file):
-    # delete temp file if created
+    """Delete temp file if created."""
     if f'{id(trainer)}.py' in file:  # if temp_file suffix in file
         os.remove(file)

ultralytics/yolo/utils/downloads.py

@@ -21,7 +21,7 @@ GITHUB_ASSET_STEMS = [Path(k).stem for k in GITHUB_ASSET_NAMES]
 
 
 def is_url(url, check=True):
-    # Check if string is URL and check if URL exists
+    """Check if string is URL and check if URL exists."""
     with contextlib.suppress(Exception):
         url = str(url)
         result = parse.urlparse(url)
@@ -141,11 +141,11 @@ def safe_download(url,
 
 
 def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
-    # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc.
+    """Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc."""
     from ultralytics.yolo.utils import SETTINGS  # scoped for circular import
 
     def github_assets(repository, version='latest'):
-        # Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov8s.pt', ...])
+        """Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov8s.pt', ...])."""
         if version != 'latest':
             version = f'tags/{version}'  # i.e. tags/v6.2
         response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json()  # github api

ultralytics/yolo/utils/files.py

@@ -8,7 +8,8 @@ from pathlib import Path
 
 
 class WorkingDirectory(contextlib.ContextDecorator):
-    # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
+    """Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager."""
+
     def __init__(self, new_dir):
         self.dir = new_dir  # new dir
         self.cwd = Path.cwd().resolve()  # current dir
@@ -56,19 +57,19 @@ def increment_path(path, exist_ok=False, sep='', mkdir=False):
 
 
 def file_age(path=__file__):
-    # Return days since last file update
+    """Return days since last file update."""
     dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime))  # delta
     return dt.days  # + dt.seconds / 86400  # fractional days
 
 
 def file_date(path=__file__):
-    # Return human-readable file modification date, i.e. '2021-3-26'
+    """Return human-readable file modification date, i.e. '2021-3-26'."""
     t = datetime.fromtimestamp(Path(path).stat().st_mtime)
     return f'{t.year}-{t.month}-{t.day}'
 
 
 def file_size(path):
-    # Return file/dir size (MB)
+    """Return file/dir size (MB)."""
     if isinstance(path, (str, Path)):
         mb = 1 << 20  # bytes to MiB (1024 ** 2)
         path = Path(path)
@@ -80,6 +81,6 @@ def file_size(path):
 
 
 def get_latest_run(search_dir='.'):
-    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
+    """Return path to most recent 'last.pt' in /runs (i.e. to --resume from)."""
     last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
     return max(last_list, key=os.path.getctime) if last_list else ''

ultralytics/yolo/utils/instance.py

@@ -11,7 +11,8 @@ from .ops import ltwh2xywh, ltwh2xyxy, resample_segments, xywh2ltwh, xywh2xyxy,
 
 
 def _ntuple(n):
-    # From PyTorch internals
+    """From PyTorch internals."""
+
     def parse(x):
         return x if isinstance(x, abc.Iterable) else tuple(repeat(x, n))
 
@@ -29,7 +30,7 @@ __all__ = 'Bboxes',  # tuple or list
 
 
 class Bboxes:
-    """Now only numpy is supported"""
+    """Now only numpy is supported."""
 
     def __init__(self, bboxes, format='xyxy') -> None:
         assert format in _formats
@@ -80,7 +81,7 @@ class Bboxes:
         return (self.bboxes[:, 2] - self.bboxes[:, 0]) * (self.bboxes[:, 3] - self.bboxes[:, 1])
 
     # def denormalize(self, w, h):
-    #     if not self.normalized:
+    #    if not self.normalized:
     #         return
     #     assert (self.bboxes <= 1.0).all()
     #     self.bboxes[:, 0::2] *= w
@@ -207,7 +208,7 @@ class Instances:
         self._bboxes.areas()
 
     def scale(self, scale_w, scale_h, bbox_only=False):
-        """this might be similar with denormalize func but without normalized sign"""
+        """this might be similar with denormalize func but without normalized sign."""
         self._bboxes.mul(scale=(scale_w, scale_h, scale_w, scale_h))
         if bbox_only:
             return
@@ -240,7 +241,7 @@ class Instances:
         self.normalized = True
 
     def add_padding(self, padw, padh):
-        # handle rect and mosaic situation
+        """Handle rect and mosaic situation."""
         assert not self.normalized, 'you should add padding with absolute coordinates.'
         self._bboxes.add(offset=(padw, padh, padw, padh))
         self.segments[..., 0] += padw

ultralytics/yolo/utils/loss.py

@@ -9,7 +9,8 @@ from .tal import bbox2dist
 
 
 class VarifocalLoss(nn.Module):
-    # Varifocal loss by Zhang et al. https://arxiv.org/abs/2008.13367
+    """Varifocal loss by Zhang et al. https://arxiv.org/abs/2008.13367."""
+
     def __init__(self):
         super().__init__()
 
@@ -29,7 +30,7 @@ class BboxLoss(nn.Module):
         self.use_dfl = use_dfl
 
     def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask):
-        # IoU loss
+        """IoU loss."""
         weight = torch.masked_select(target_scores.sum(-1), fg_mask).unsqueeze(-1)
         iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True)
         loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum
@@ -46,7 +47,7 @@ class BboxLoss(nn.Module):
 
     @staticmethod
     def _df_loss(pred_dist, target):
-        # Return sum of left and right DFL losses
+        """Return sum of left and right DFL losses."""
         # Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
         tl = target.long()  # target left
         tr = tl + 1  # target right

ultralytics/yolo/utils/metrics.py

@@ -16,9 +16,9 @@ from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept, plt_settings
 OKS_SIGMA = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0
 
 
-# boxes
+# Boxes
 def box_area(box):
-    # box = xyxy(4,n)
+    """Return box area, where box shape is xyxy(4,n)."""
     return (box[2] - box[0]) * (box[3] - box[1])
 
 
@@ -175,9 +175,10 @@ def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#iss
     return 1.0 - 0.5 * eps, 0.5 * eps
 
 
-# losses
+# Losses
 class FocalLoss(nn.Module):
-    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+    """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
+
     def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
         super().__init__()
         self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
@@ -341,7 +342,7 @@ class ConfusionMatrix:
 
 
 def smooth(y, f=0.05):
-    # Box filter of fraction f
+    """Box filter of fraction f."""
     nf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)
     p = np.ones(nf // 2)  # ones padding
     yp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y padded
@@ -350,7 +351,7 @@ def smooth(y, f=0.05):
 
 @plt_settings()
 def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
-    # Precision-recall curve
+    """Plots a precision-recall curve."""
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
     py = np.stack(py, axis=1)
 
@@ -373,7 +374,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
 
 @plt_settings()
 def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
-    # Metric-confidence curve
+    """Plots a metric-confidence curve."""
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
 
     if 0 < len(names) < 21:  # display per-class legend if < 21 classes
@@ -614,23 +615,23 @@ class Metric(SimpleClass):
         return self.all_ap.mean() if len(self.all_ap) else 0.0
 
     def mean_results(self):
-        """Mean of results, return mp, mr, map50, map"""
+        """Mean of results, return mp, mr, map50, map."""
         return [self.mp, self.mr, self.map50, self.map]
 
     def class_result(self, i):
-        """class-aware result, return p[i], r[i], ap50[i], ap[i]"""
+        """class-aware result, return p[i], r[i], ap50[i], ap[i]."""
         return self.p[i], self.r[i], self.ap50[i], self.ap[i]
 
     @property
     def maps(self):
-        """mAP of each class"""
+        """mAP of each class."""
         maps = np.zeros(self.nc) + self.map
         for i, c in enumerate(self.ap_class_index):
             maps[c] = self.ap[i]
         return maps
 
     def fitness(self):
-        # Model fitness as a weighted combination of metrics
+        """Model fitness as a weighted combination of metrics."""
         w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
         return (np.array(self.mean_results()) * w).sum()
 
@@ -800,7 +801,7 @@ class SegmentMetrics(SimpleClass):
 
     @property
     def ap_class_index(self):
-        # boxes and masks have the same ap_class_index
+        """Boxes and masks have the same ap_class_index."""
         return self.box.ap_class_index
 
     @property
@@ -926,7 +927,7 @@ class ClassifyMetrics(SimpleClass):
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
 
     def process(self, targets, pred):
-        # target classes and predicted classes
+        """Target classes and predicted classes."""
         pred, targets = torch.cat(pred), torch.cat(targets)
         correct = (targets[:, None] == pred).float()
         acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1)  # (top1, top5) accuracy

ultralytics/yolo/utils/ops.py

@@ -246,7 +246,7 @@ def non_max_suppression(
         i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
         i = i[:max_det]  # limit detections
         if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+            # Update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
             iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
             weights = iou * scores[None]  # box weights
             x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes

ultralytics/yolo/utils/plotting.py

@@ -21,7 +21,7 @@ from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
 class Colors:
     # Ultralytics color palette https://ultralytics.com/
     def __init__(self):
-        # hex = matplotlib.colors.TABLEAU_COLORS.values()
+        """Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
         hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
                 '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
         self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
@@ -63,7 +63,7 @@ class Annotator:
         else:  # use cv2
             self.im = im
         self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
-        # pose
+        # Pose
         self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9],
                          [8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
 
@@ -115,7 +115,7 @@ class Annotator:
             alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
         """
         if self.pil:
-            # convert to numpy first
+            # Convert to numpy first
             self.im = np.asarray(self.im).copy()
         if len(masks) == 0:
             self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
@@ -136,7 +136,7 @@ class Annotator:
         im_mask_np = im_mask.byte().cpu().numpy()
         self.im[:] = im_mask_np if retina_masks else scale_image(im_mask_np, self.im.shape)
         if self.pil:
-            # convert im back to PIL and update draw
+            # Convert im back to PIL and update draw
             self.fromarray(self.im)
 
     def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
@@ -152,7 +152,7 @@ class Annotator:
         Note: `kpt_line=True` currently only supports human pose plotting.
         """
         if self.pil:
-            # convert to numpy first
+            # Convert to numpy first
             self.im = np.asarray(self.im).copy()
         nkpt, ndim = kpts.shape
         is_pose = nkpt == 17 and ndim == 3
@@ -183,11 +183,11 @@ class Annotator:
                     continue
                 cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
         if self.pil:
-            # convert im back to PIL and update draw
+            # Convert im back to PIL and update draw
             self.fromarray(self.im)
 
     def rectangle(self, xy, fill=None, outline=None, width=1):
-        # Add rectangle to image (PIL-only)
+        """Add rectangle to image (PIL-only)."""
         self.draw.rectangle(xy, fill, outline, width)
 
     def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
@@ -202,12 +202,12 @@ class Annotator:
             cv2.putText(self.im, text, xy, 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
 
     def fromarray(self, im):
-        # Update self.im from a numpy array
+        """Update self.im from a numpy array."""
         self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
         self.draw = ImageDraw.Draw(self.im)
 
     def result(self):
-        # Return annotated image as array
+        """Return annotated image as array."""
         return np.asarray(self.im)
 
 
@@ -217,18 +217,18 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
     import pandas as pd
     import seaborn as sn
 
-    # plot dataset labels
+    # Plot dataset labels
     LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
     b = boxes.transpose()  # classes, boxes
     nc = int(cls.max() + 1)  # number of classes
     x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
 
-    # seaborn correlogram
+    # Seaborn correlogram
     sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
     plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
     plt.close()
 
-    # matplotlib labels
+    # Matplotlib labels
     ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
     y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
     with contextlib.suppress(Exception):  # color histogram bars by class
@@ -242,7 +242,7 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
     sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
     sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
 
-    # rectangles
+    # Rectangles
     boxes[:, 0:2] = 0.5  # center
     boxes = xywh2xyxy(boxes) * 1000
     img = Image.fromarray(np.ones((1000, 1000, 3), dtype=np.uint8) * 255)
@@ -401,7 +401,7 @@ def plot_images(images,
 
 @plt_settings()
 def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False):
-    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
+    """Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')."""
     import pandas as pd
     save_dir = Path(file).parent if file else Path(dir)
     if segment:
@@ -436,7 +436,7 @@ def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False):
 
 
 def output_to_target(output, max_det=300):
-    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
+    """Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting."""
     targets = []
     for i, o in enumerate(output):
         box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)

ultralytics/yolo/utils/tal.py

@@ -48,7 +48,7 @@ def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
         is_max_overlaps = is_max_overlaps.permute(0, 2, 1).to(overlaps.dtype)  # (b, n_max_boxes, h*w)
         mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos)  # (b, n_max_boxes, h*w)
         fg_mask = mask_pos.sum(-2)
-    # find each grid serve which gt(index)
+    # Find each grid serve which gt(index)
     target_gt_idx = mask_pos.argmax(-2)  # (b, h*w)
     return target_gt_idx, fg_mask, mask_pos
 
@@ -112,10 +112,10 @@ class TaskAlignedAssigner(nn.Module):
 
         target_gt_idx, fg_mask, mask_pos = select_highest_overlaps(mask_pos, overlaps, self.n_max_boxes)
 
-        # assigned target
+        # Assigned target
         target_labels, target_bboxes, target_scores = self.get_targets(gt_labels, gt_bboxes, target_gt_idx, fg_mask)
 
-        # normalize
+        # Normalize
         align_metric *= mask_pos
         pos_align_metrics = align_metric.amax(axis=-1, keepdim=True)  # b, max_num_obj
         pos_overlaps = (overlaps * mask_pos).amax(axis=-1, keepdim=True)  # b, max_num_obj
@@ -125,13 +125,13 @@ class TaskAlignedAssigner(nn.Module):
         return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
 
     def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt):
-        # get in_gts mask, (b, max_num_obj, h*w)
+        """Get in_gts mask, (b, max_num_obj, h*w)."""
         mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes)
-        # get anchor_align metric, (b, max_num_obj, h*w)
+        # Get anchor_align metric, (b, max_num_obj, h*w)
         align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts * mask_gt)
-        # get topk_metric mask, (b, max_num_obj, h*w)
+        # Get topk_metric mask, (b, max_num_obj, h*w)
         mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.repeat([1, 1, self.topk]).bool())
-        # merge all mask to a final mask, (b, max_num_obj, h*w)
+        # Merge all mask to a final mask, (b, max_num_obj, h*w)
         mask_pos = mask_topk * mask_in_gts * mask_gt
 
         return mask_pos, align_metric, overlaps
@@ -145,7 +145,7 @@ class TaskAlignedAssigner(nn.Module):
         ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long)  # 2, b, max_num_obj
         ind[0] = torch.arange(end=self.bs).view(-1, 1).repeat(1, self.n_max_boxes)  # b, max_num_obj
         ind[1] = gt_labels.long().squeeze(-1)  # b, max_num_obj
-        # get the scores of each grid for each gt cls
+        # Get the scores of each grid for each gt cls
         bbox_scores[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt]  # b, max_num_obj, h*w
 
         # (b, max_num_obj, 1, 4), (b, 1, h*w, 4)

ultralytics/yolo/utils/torch_utils.py

@@ -30,7 +30,7 @@ TORCH_2_X = check_version(torch.__version__, minimum='2.0')
 
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
-    # Decorator to make all processes in distributed training wait for each local_master to do something
+    """Decorator to make all processes in distributed training wait for each local_master to do something."""
     initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
     if initialized and local_rank not in (-1, 0):
         dist.barrier(device_ids=[local_rank])
@@ -40,7 +40,8 @@ def torch_distributed_zero_first(local_rank: int):
 
 
 def smart_inference_mode():
-    # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
+    """Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator."""
+
     def decorate(fn):
         return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)
 
@@ -48,7 +49,7 @@ def smart_inference_mode():
 
 
 def select_device(device='', batch=0, newline=False, verbose=True):
-    # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
+    """Selects PyTorch Device. Options are device = None or 'cpu' or 0 or '0' or '0,1,2,3'."""
     s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
     device = str(device).lower()
     for remove in 'cuda:', 'none', '(', ')', '[', ']', "'", ' ':
@@ -84,7 +85,7 @@ def select_device(device='', batch=0, newline=False, verbose=True):
             s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n"  # bytes to MB
         arg = 'cuda:0'
     elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available() and TORCH_2_X:
-        # prefer MPS if available
+        # Prefer MPS if available
         s += 'MPS\n'
         arg = 'mps'
     else:  # revert to CPU
@@ -97,14 +98,14 @@ def select_device(device='', batch=0, newline=False, verbose=True):
 
 
 def time_sync():
-    # PyTorch-accurate time
+    """PyTorch-accurate time."""
     if torch.cuda.is_available():
         torch.cuda.synchronize()
     return time.time()
 
 
 def fuse_conv_and_bn(conv, bn):
-    # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+    """Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/."""
     fusedconv = nn.Conv2d(conv.in_channels,
                           conv.out_channels,
                           kernel_size=conv.kernel_size,
@@ -128,7 +129,7 @@ def fuse_conv_and_bn(conv, bn):
 
 
 def fuse_deconv_and_bn(deconv, bn):
-    # Fuse ConvTranspose2d() and BatchNorm2d() layers
+    """Fuse ConvTranspose2d() and BatchNorm2d() layers."""
     fuseddconv = nn.ConvTranspose2d(deconv.in_channels,
                                     deconv.out_channels,
                                     kernel_size=deconv.kernel_size,
@@ -139,7 +140,7 @@ def fuse_deconv_and_bn(deconv, bn):
                                     groups=deconv.groups,
                                     bias=True).requires_grad_(False).to(deconv.weight.device)
 
-    # prepare filters
+    # Prepare filters
     w_deconv = deconv.weight.clone().view(deconv.out_channels, -1)
     w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
     fuseddconv.weight.copy_(torch.mm(w_bn, w_deconv).view(fuseddconv.weight.shape))
@@ -153,7 +154,7 @@ def fuse_deconv_and_bn(deconv, bn):
 
 
 def model_info(model, detailed=False, verbose=True, imgsz=640):
-    # Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]
+    """Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]."""
     if not verbose:
         return
     n_p = get_num_params(model)
@@ -174,17 +175,17 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
 
 
 def get_num_params(model):
-    # Return the total number of parameters in a YOLO model
+    """Return the total number of parameters in a YOLO model."""
     return sum(x.numel() for x in model.parameters())
 
 
 def get_num_gradients(model):
-    # Return the total number of parameters with gradients in a YOLO model
+    """Return the total number of parameters with gradients in a YOLO model."""
     return sum(x.numel() for x in model.parameters() if x.requires_grad)
 
 
 def get_flops(model, imgsz=640):
-    # Return a YOLO model's FLOPs
+    """Return a YOLO model's FLOPs."""
     try:
         model = de_parallel(model)
         p = next(model.parameters())
@@ -199,7 +200,7 @@ def get_flops(model, imgsz=640):
 
 
 def initialize_weights(model):
-    # Initialize model weights to random values
+    """Initialize model weights to random values."""
     for m in model.modules():
         t = type(m)
         if t is nn.Conv2d:
@@ -224,7 +225,7 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
 
 
 def make_divisible(x, divisor):
-    # Returns nearest x divisible by divisor
+    """Returns nearest x divisible by divisor."""
     if isinstance(divisor, torch.Tensor):
         divisor = int(divisor.max())  # to int
     return math.ceil(x / divisor) * divisor
@@ -240,7 +241,7 @@ def copy_attr(a, b, include=(), exclude=()):
 
 
 def get_latest_opset():
-    # Return second-most (for maturity) recently supported ONNX opset by this version of torch
+    """Return second-most (for maturity) recently supported ONNX opset by this version of torch."""
     return max(int(k[14:]) for k in vars(torch.onnx) if 'symbolic_opset' in k) - 1  # opset
 
 
@@ -250,22 +251,22 @@ def intersect_dicts(da, db, exclude=()):
 
 
 def is_parallel(model):
-    # Returns True if model is of type DP or DDP
+    """Returns True if model is of type DP or DDP."""
     return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))
 
 
 def de_parallel(model):
-    # De-parallelize a model: returns single-GPU model if model is of type DP or DDP
+    """De-parallelize a model: returns single-GPU model if model is of type DP or DDP."""
     return model.module if is_parallel(model) else model
 
 
 def one_cycle(y1=0.0, y2=1.0, steps=100):
-    # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
+    """Returns a lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf."""
     return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
 
 
 def init_seeds(seed=0, deterministic=False):
-    # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
+    """Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html."""
     random.seed(seed)
     np.random.seed(seed)
     torch.manual_seed(seed)
@@ -280,14 +281,14 @@ def init_seeds(seed=0, deterministic=False):
 
 
 class ModelEMA:
-    """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
+    """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
     Keeps a moving average of everything in the model state_dict (parameters and buffers)
     For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
     To disable EMA set the `enabled` attribute to `False`.
     """
 
     def __init__(self, model, decay=0.9999, tau=2000, updates=0):
-        # Create EMA
+        """Create EMA."""
         self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA
         self.updates = updates  # number of EMA updates
         self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)
@@ -296,7 +297,7 @@ class ModelEMA:
         self.enabled = True
 
     def update(self, model):
-        # Update EMA parameters
+        """Update EMA parameters."""
         if self.enabled:
             self.updates += 1
             d = self.decay(self.updates)

ultralytics/yolo/v8/classify/train.py

@@ -46,7 +46,7 @@ class ClassificationTrainer(BaseTrainer):
         """
         load/create/download model for any task
         """
-        # classification models require special handling
+        # Classification models require special handling
 
         if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
             return

ultralytics/yolo/v8/detect/train.py

@@ -22,8 +22,8 @@ from ultralytics.yolo.utils.torch_utils import de_parallel
 class DetectionTrainer(BaseTrainer):
 
     def get_dataloader(self, dataset_path, batch_size, rank=0, mode='train'):
-        # TODO: manage splits differently
+        """TODO: manage splits differently."""
-        # calculate stride - check if model is initialized
+        # Calculate stride - check if model is initialized
         gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
         return create_dataloader(path=dataset_path,
                                  imgsz=self.args.imgsz,
@@ -48,7 +48,7 @@ class DetectionTrainer(BaseTrainer):
         return batch
 
     def set_model_attributes(self):
-        # nl = de_parallel(self.model).model[-1].nl  # number of detection layers (to scale hyps)
+        """nl = de_parallel(self.model).model[-1].nl  # number of detection layers (to scale hyps)."""
         # self.args.box *= 3 / nl  # scale to layers
         # self.args.cls *= self.data["nc"] / 80 * 3 / nl  # scale to classes and layers
         # self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers

ultralytics/yolo/v8/detect/val.py

@@ -67,7 +67,7 @@ class DetectionValidator(BaseValidator):
         return preds
 
     def update_metrics(self, preds, batch):
-        # Metrics
+        """Metrics."""
         for si, pred in enumerate(preds):
             idx = batch['batch_idx'] == si
             cls = batch['cls'][idx]
@@ -164,8 +164,8 @@ class DetectionValidator(BaseValidator):
         return torch.tensor(correct, dtype=torch.bool, device=detections.device)
 
     def get_dataloader(self, dataset_path, batch_size):
-        # TODO: manage splits differently
+        """TODO: manage splits differently."""
-        # calculate stride - check if model is initialized
+        # Calculate stride - check if model is initialized
         gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
         return create_dataloader(path=dataset_path,
                                  imgsz=self.args.imgsz,

ultralytics/yolo/v8/pose/val.py

@@ -47,7 +47,7 @@ class PoseValidator(DetectionValidator):
         self.sigma = OKS_SIGMA if is_pose else np.ones(nkpt) / nkpt
 
     def update_metrics(self, preds, batch):
-        # Metrics
+        """Metrics."""
         for si, pred in enumerate(preds):
             idx = batch['batch_idx'] == si
             cls = batch['cls'][idx]

ultralytics/yolo/v8/segment/predict.py

@@ -10,7 +10,7 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
 class SegmentationPredictor(DetectionPredictor):
 
     def postprocess(self, preds, img, orig_imgs):
-        # TODO: filter by classes
+        """TODO: filter by classes."""
         p = ops.non_max_suppression(preds[0],
                                     self.args.conf,
                                     self.args.iou,

ultralytics/yolo/v8/segment/train.py

@@ -140,7 +140,7 @@ class SegLoss(Loss):
         return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
 
     def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
-        # Mask loss for one image
+        """Mask loss for one image."""
         pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:])  # (n, 32) @ (32,80,80) -> (n,80,80)
         loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
         return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()

ultralytics/yolo/v8/segment/val.py

@@ -52,7 +52,7 @@ class SegmentationValidator(DetectionValidator):
         return p, proto
 
     def update_metrics(self, preds, batch):
-        # Metrics
+        """Metrics."""
         for si, (pred, proto) in enumerate(zip(preds[0], preds[1])):
             idx = batch['batch_idx'] == si
             cls = batch['cls'][idx]
@@ -179,7 +179,7 @@ class SegmentationValidator(DetectionValidator):
         self.plot_masks.clear()
 
     def pred_to_json(self, predn, filename, pred_masks):
-        # Save one JSON result
+        """Save one JSON result."""
         # Example result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
         from pycocotools.mask import encode  # noqa