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Ruff format docstring Python code (#15792)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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94
ultralytics/engine/model.py
View file

@ -72,11 +72,11 @@ class Model(nn.Module):
Examples:
>>> from ultralytics import YOLO
>>> model = YOLO('yolov8n.pt')
>>> results = model.predict('image.jpg')
>>> model.train(data='coco128.yaml', epochs=3)
>>> model = YOLO("yolov8n.pt")
>>> results = model.predict("image.jpg")
>>> model.train(data="coco128.yaml", epochs=3)
>>> metrics = model.val()
>>> model.export(format='onnx')
>>> model.export(format="onnx")
"""
def __init__(
@ -166,8 +166,8 @@ class Model(nn.Module):
Results object.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model('https://ultralytics.com/images/bus.jpg')
>>> model = YOLO("yolov8n.pt")
>>> results = model("https://ultralytics.com/images/bus.jpg")
>>> for r in results:
... print(f"Detected {len(r)} objects in image")
"""
@ -188,9 +188,9 @@ class Model(nn.Module):
(bool): True if the model string is a valid Triton Server URL, False otherwise.
Examples:
>>> Model.is_triton_model('http://localhost:8000/v2/models/yolov8n')
>>> Model.is_triton_model("http://localhost:8000/v2/models/yolov8n")
True
>>> Model.is_triton_model('yolov8n.pt')
>>> Model.is_triton_model("yolov8n.pt")
False
"""
from urllib.parse import urlsplit
@ -253,7 +253,7 @@ class Model(nn.Module):
Examples:
>>> model = Model()
>>> model._new('yolov8n.yaml', task='detect', verbose=True)
>>> model._new("yolov8n.yaml", task="detect", verbose=True)
"""
cfg_dict = yaml_model_load(cfg)
self.cfg = cfg
@ -284,8 +284,8 @@ class Model(nn.Module):
Examples:
>>> model = Model()
>>> model._load('yolov8n.pt')
>>> model._load('path/to/weights.pth', task='detect')
>>> model._load("yolov8n.pt")
>>> model._load("path/to/weights.pth", task="detect")
"""
if weights.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://")):
weights = checks.check_file(weights, download_dir=SETTINGS["weights_dir"]) # download and return local file
@ -348,7 +348,7 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model('yolov8n.pt')
>>> model = Model("yolov8n.pt")
>>> model.reset_weights()
"""
self._check_is_pytorch_model()
@ -377,8 +377,8 @@ class Model(nn.Module):
Examples:
>>> model = Model()
>>> model.load('yolov8n.pt')
>>> model.load(Path('path/to/weights.pt'))
>>> model.load("yolov8n.pt")
>>> model.load(Path("path/to/weights.pt"))
"""
self._check_is_pytorch_model()
if isinstance(weights, (str, Path)):
@ -402,8 +402,8 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model('yolov8n.pt')
>>> model.save('my_model.pt')
>>> model = Model("yolov8n.pt")
>>> model.save("my_model.pt")
"""
self._check_is_pytorch_model()
from copy import deepcopy
@ -439,7 +439,7 @@ class Model(nn.Module):
TypeError: If the model is not a PyTorch model.
Examples:
>>> model = Model('yolov8n.pt')
>>> model = Model("yolov8n.pt")
>>> model.info() # Prints model summary
>>> info_list = model.info(detailed=True, verbose=False) # Returns detailed info as a list
"""
@ -494,8 +494,8 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> image = 'https://ultralytics.com/images/bus.jpg'
>>> model = YOLO("yolov8n.pt")
>>> image = "https://ultralytics.com/images/bus.jpg"
>>> embeddings = model.embed(image)
>>> print(embeddings[0].shape)
"""
@ -531,8 +531,8 @@ class Model(nn.Module):
Results object.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.predict(source='path/to/image.jpg', conf=0.25)
>>> model = YOLO("yolov8n.pt")
>>> results = model.predict(source="path/to/image.jpg", conf=0.25)
>>> for r in results:
... print(r.boxes.data) # print detection bounding boxes
@ -592,8 +592,8 @@ class Model(nn.Module):
AttributeError: If the predictor does not have registered trackers.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.track(source='path/to/video.mp4', show=True)
>>> model = YOLO("yolov8n.pt")
>>> results = model.track(source="path/to/video.mp4", show=True)
>>> for r in results:
... print(r.boxes.id) # print tracking IDs
@ -635,8 +635,8 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.val(data='coco128.yaml', imgsz=640)
>>> model = YOLO("yolov8n.pt")
>>> results = model.val(data="coco128.yaml", imgsz=640)
>>> print(results.box.map) # Print mAP50-95
"""
custom = {"rect": True} # method defaults
@ -677,8 +677,8 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.benchmark(data='coco8.yaml', imgsz=640, half=True)
>>> model = YOLO("yolov8n.pt")
>>> results = model.benchmark(data="coco8.yaml", imgsz=640, half=True)
>>> print(results)
"""
self._check_is_pytorch_model()
@ -727,8 +727,8 @@ class Model(nn.Module):
RuntimeError: If the export process fails due to errors.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model.export(format='onnx', dynamic=True, simplify=True)
>>> model = YOLO("yolov8n.pt")
>>> model.export(format="onnx", dynamic=True, simplify=True)
'path/to/exported/model.onnx'
"""
self._check_is_pytorch_model()
@ -782,8 +782,8 @@ class Model(nn.Module):
ModuleNotFoundError: If the HUB SDK is not installed.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.train(data='coco128.yaml', epochs=3)
>>> model = YOLO("yolov8n.pt")
>>> results = model.train(data="coco128.yaml", epochs=3)
"""
self._check_is_pytorch_model()
if hasattr(self.session, "model") and self.session.model.id: # Ultralytics HUB session with loaded model
@ -847,7 +847,7 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model = YOLO("yolov8n.pt")
>>> results = model.tune(use_ray=True, iterations=20)
>>> print(results)
"""
@ -907,7 +907,7 @@ class Model(nn.Module):
AttributeError: If the model or predictor does not have a 'names' attribute.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model = YOLO("yolov8n.pt")
>>> print(model.names)
{0: 'person', 1: 'bicycle', 2: 'car', ...}
"""
@ -957,7 +957,7 @@ class Model(nn.Module):
(object | None): The transform object of the model if available, otherwise None.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model = YOLO("yolov8n.pt")
>>> transforms = model.transforms
>>> if transforms:
... print(f"Model transforms: {transforms}")
@ -986,9 +986,9 @@ class Model(nn.Module):
Examples:
>>> def on_train_start(trainer):
... print("Training is starting!")
>>> model = YOLO('yolov8n.pt')
>>> model = YOLO("yolov8n.pt")
>>> model.add_callback("on_train_start", on_train_start)
>>> model.train(data='coco128.yaml', epochs=1)
>>> model.train(data="coco128.yaml", epochs=1)
"""
self.callbacks[event].append(func)
@ -1005,9 +1005,9 @@ class Model(nn.Module):
recognized by the Ultralytics callback system.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model.add_callback('on_train_start', lambda: print('Training started'))
>>> model.clear_callback('on_train_start')
>>> model = YOLO("yolov8n.pt")
>>> model.add_callback("on_train_start", lambda: print("Training started"))
>>> model.clear_callback("on_train_start")
>>> # All callbacks for 'on_train_start' are now removed
Notes:
@ -1035,8 +1035,8 @@ class Model(nn.Module):
modifications, ensuring consistent behavior across different runs or experiments.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model.add_callback('on_train_start', custom_function)
>>> model = YOLO("yolov8n.pt")
>>> model.add_callback("on_train_start", custom_function)
>>> model.reset_callbacks()
# All callbacks are now reset to their default functions
"""
@ -1059,7 +1059,7 @@ class Model(nn.Module):
(dict): A new dictionary containing only the specified include keys from the input arguments.
Examples:
>>> original_args = {'imgsz': 640, 'data': 'coco.yaml', 'task': 'detect', 'batch': 16, 'epochs': 100}
>>> original_args = {"imgsz": 640, "data": "coco.yaml", "task": "detect", "batch": 16, "epochs": 100}
>>> reset_args = Model._reset_ckpt_args(original_args)
>>> print(reset_args)
{'imgsz': 640, 'data': 'coco.yaml', 'task': 'detect'}
@ -1090,9 +1090,9 @@ class Model(nn.Module):
NotImplementedError: If the specified key is not supported for the current task.
Examples:
>>> model = Model(task='detect')
>>> predictor = model._smart_load('predictor')
>>> trainer = model._smart_load('trainer')
>>> model = Model(task="detect")
>>> predictor = model._smart_load("predictor")
>>> trainer = model._smart_load("trainer")
Notes:
- This method is typically used internally by other methods of the Model class.
@ -1128,8 +1128,8 @@ class Model(nn.Module):
Examples:
>>> model = Model()
>>> task_map = model.task_map
>>> detect_class_map = task_map['detect']
>>> segment_class_map = task_map['segment']
>>> detect_class_map = task_map["detect"]
>>> segment_class_map = task_map["segment"]
Note:
The actual implementation of this method may vary depending on the specific tasks and

56
ultralytics/engine/results.py
View file

@ -143,7 +143,7 @@ class BaseTensor(SimpleClass):
Examples:
>>> base_tensor = BaseTensor(torch.randn(3, 4), orig_shape=(480, 640))
>>> cuda_tensor = base_tensor.to('cuda')
>>> cuda_tensor = base_tensor.to("cuda")
>>> float16_tensor = base_tensor.to(dtype=torch.float16)
"""
return self.__class__(torch.as_tensor(self.data).to(*args, **kwargs), self.orig_shape)
@ -223,7 +223,7 @@ class Results(SimpleClass):
>>> for result in results:
... print(result.boxes) # Print detection boxes
... result.show() # Display the annotated image
... result.save(filename='result.jpg') # Save annotated image
... result.save(filename="result.jpg") # Save annotated image
"""
def __init__(
@ -280,7 +280,7 @@ class Results(SimpleClass):
(Results): A new Results object containing the specified subset of inference results.
Examples:
>>> results = model('path/to/image.jpg') # Perform inference
>>> results = model("path/to/image.jpg") # Perform inference
>>> single_result = results[0] # Get the first result
>>> subset_results = results[1:4] # Get a slice of results
"""
@ -319,7 +319,7 @@ class Results(SimpleClass):
obb (torch.Tensor | None): A tensor of shape (N, 5) containing oriented bounding box coordinates.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> new_boxes = torch.tensor([[100, 100, 200, 200, 0.9, 0]])
>>> results[0].update(boxes=new_boxes)
"""
@ -370,7 +370,7 @@ class Results(SimpleClass):
(Results): A new Results object with all tensor attributes on CPU memory.
Examples:
>>> results = model('path/to/image.jpg') # Perform inference
>>> results = model("path/to/image.jpg") # Perform inference
>>> cpu_result = results[0].cpu() # Move the first result to CPU
>>> print(cpu_result.boxes.device) # Output: cpu
"""
@ -384,7 +384,7 @@ class Results(SimpleClass):
(Results): A new Results object with all tensors converted to numpy arrays.
Examples:
>>> results = model('path/to/image.jpg')
>>> results = model("path/to/image.jpg")
>>> numpy_result = results[0].numpy()
>>> type(numpy_result.boxes.data)
<class 'numpy.ndarray'>
@ -488,7 +488,7 @@ class Results(SimpleClass):
(np.ndarray): Annotated image as a numpy array.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> for result in results:
... im = result.plot()
... im.show()
@ -578,7 +578,7 @@ class Results(SimpleClass):
**kwargs (Any): Arbitrary keyword arguments to be passed to the `plot()` method.
Examples:
>>> results = model('path/to/image.jpg')
>>> results = model("path/to/image.jpg")
>>> results[0].show() # Display the first result
>>> for result in results:
... result.show() # Display all results
@ -599,12 +599,12 @@ class Results(SimpleClass):
**kwargs (Any): Arbitrary keyword arguments to be passed to the `plot` method.
Examples:
>>> results = model('path/to/image.jpg')
>>> results = model("path/to/image.jpg")
>>> for result in results:
... result.save('annotated_image.jpg')
... result.save("annotated_image.jpg")
>>> # Or with custom plot arguments
>>> for result in results:
... result.save('annotated_image.jpg', conf=False, line_width=2)
... result.save("annotated_image.jpg", conf=False, line_width=2)
"""
if not filename:
filename = f"results_{Path(self.path).name}"
@ -623,7 +623,7 @@ class Results(SimpleClass):
number of detections per class. For classification tasks, it includes the top 5 class probabilities.
Examples:
>>> results = model('path/to/image.jpg')
>>> results = model("path/to/image.jpg")
>>> for result in results:
... print(result.verbose())
2 persons, 1 car, 3 traffic lights,
@ -660,7 +660,7 @@ class Results(SimpleClass):
Examples:
>>> from ultralytics import YOLO
>>> model = YOLO('yolov8n.pt')
>>> model = YOLO("yolov8n.pt")
>>> results = model("path/to/image.jpg")
>>> for result in results:
... result.save_txt("output.txt")
@ -757,7 +757,7 @@ class Results(SimpleClass):
task type (classification or detection) and available information (boxes, masks, keypoints).
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> summary = results[0].summary()
>>> print(summary)
"""
@ -919,7 +919,7 @@ class Boxes(BaseTensor):
coordinates in [x1, y1, x2, y2] format, where n is the number of boxes.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> boxes = results[0].boxes
>>> xyxy = boxes.xyxy
>>> print(xyxy)
@ -953,7 +953,7 @@ class Boxes(BaseTensor):
The shape is (N,), where N is the number of boxes.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> boxes = results[0].boxes
>>> class_ids = boxes.cls
>>> print(class_ids) # tensor([0., 2., 1.])
@ -970,7 +970,7 @@ class Boxes(BaseTensor):
otherwise None. Shape is (N,) where N is the number of boxes.
Examples:
>>> results = model.track('path/to/video.mp4')
>>> results = model.track("path/to/video.mp4")
>>> for result in results:
... boxes = result.boxes
... if boxes.is_track:
@ -1116,7 +1116,7 @@ class Masks(BaseTensor):
mask contour.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> masks = results[0].masks
>>> normalized_coords = masks.xyn
>>> print(normalized_coords[0]) # Normalized coordinates of the first mask
@ -1141,7 +1141,7 @@ class Masks(BaseTensor):
number of points in the segment.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> masks = results[0].masks
>>> xy_coords = masks.xy
>>> print(len(xy_coords)) # Number of masks
@ -1223,7 +1223,7 @@ class Keypoints(BaseTensor):
the number of detections and K is the number of keypoints per detection.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> keypoints = results[0].keypoints
>>> xy = keypoints.xy
>>> print(xy.shape) # (N, K, 2)
@ -1388,7 +1388,7 @@ class Probs(BaseTensor):
(torch.Tensor | numpy.ndarray): A tensor containing the confidence score of the top 1 class.
Examples:
>>> results = model('image.jpg') # classify an image
>>> results = model("image.jpg") # classify an image
>>> probs = results[0].probs # get classification probabilities
>>> top1_confidence = probs.top1conf # get confidence of top 1 class
>>> print(f"Top 1 class confidence: {top1_confidence.item():.4f}")
@ -1410,7 +1410,7 @@ class Probs(BaseTensor):
top 5 predicted classes, sorted in descending order of probability.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> probs = results[0].probs
>>> top5_conf = probs.top5conf
>>> print(top5_conf) # Prints confidence scores for top 5 classes
@ -1497,7 +1497,7 @@ class OBB(BaseTensor):
[x_center, y_center, width, height, rotation]. The shape is (N, 5) where N is the number of boxes.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> obb = results[0].obb
>>> xywhr = obb.xywhr
>>> print(xywhr.shape)
@ -1518,7 +1518,7 @@ class OBB(BaseTensor):
for N detections, where each score is in the range [0, 1].
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> obb_result = results[0].obb
>>> confidence_scores = obb_result.conf
>>> print(confidence_scores)
@ -1535,7 +1535,7 @@ class OBB(BaseTensor):
bounding box. The shape is (N,), where N is the number of boxes.
Examples:
>>> results = model('image.jpg')
>>> results = model("image.jpg")
>>> result = results[0]
>>> obb = result.obb
>>> class_values = obb.cls
@ -1553,7 +1553,7 @@ class OBB(BaseTensor):
oriented bounding box. Returns None if tracking IDs are not available.
Examples:
>>> results = model('image.jpg', tracker=True) # Run inference with tracking
>>> results = model("image.jpg", tracker=True) # Run inference with tracking
>>> for result in results:
... if result.obb is not None:
... track_ids = result.obb.id
@ -1620,8 +1620,8 @@ class OBB(BaseTensor):
Examples:
>>> import torch
>>> from ultralytics import YOLO
>>> model = YOLO('yolov8n-obb.pt')
>>> results = model('path/to/image.jpg')
>>> model = YOLO("yolov8n-obb.pt")
>>> results = model("path/to/image.jpg")
>>> for result in results:
... obb = result.obb
... if obb is not None:

10
ultralytics/engine/tuner.py
View file

@ -12,8 +12,8 @@ Example:
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
model = YOLO("yolov8n.pt")
model.tune(data="coco8.yaml", epochs=10, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
```
"""
@ -54,15 +54,15 @@ class Tuner:
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
model = YOLO("yolov8n.pt")
model.tune(data="coco8.yaml", epochs=10, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
```
Tune with custom search space.
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
model.tune(space={key1: val1, key2: val2}) # custom search space dictionary
```
"""