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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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Glenn Jocher 2024-08-25 01:08:07 +08:00 • committed by GitHub
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63 changed files with 370 additions and 374 deletions
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ultralytics/engine/results.py
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@ -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: