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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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114
ultralytics/data/augment.py
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@ -38,7 +38,7 @@ class BaseTransform:
Examples:
>>> transform = BaseTransform()
>>> labels = {'image': np.array(...), 'instances': [...], 'semantic': np.array(...)}
>>> labels = {"image": np.array(...), "instances": [...], "semantic": np.array(...)}
>>> transformed_labels = transform(labels)
"""
@ -93,7 +93,7 @@ class BaseTransform:
Examples:
>>> transform = BaseTransform()
>>> labels = {'instances': Instances(xyxy=torch.rand(5, 4), cls=torch.randint(0, 80, (5,)))}
>>> labels = {"instances": Instances(xyxy=torch.rand(5, 4), cls=torch.randint(0, 80, (5,)))}
>>> transformed_labels = transform.apply_instances(labels)
"""
pass
@ -135,7 +135,7 @@ class BaseTransform:
Examples:
>>> transform = BaseTransform()
>>> labels = {'img': np.random.rand(640, 640, 3), 'instances': []}
>>> labels = {"img": np.random.rand(640, 640, 3), "instances": []}
>>> transformed_labels = transform(labels)
"""
self.apply_image(labels)
@ -338,6 +338,7 @@ class BaseMixTransform:
... def _mix_transform(self, labels):
... # Implement custom mix logic here
... return labels
...
... def get_indexes(self):
... return [random.randint(0, len(self.dataset) - 1) for _ in range(3)]
>>> dataset = YourDataset()
@ -421,7 +422,7 @@ class BaseMixTransform:
Examples:
>>> transform = BaseMixTransform(dataset)
>>> labels = {'image': img, 'bboxes': boxes, 'mix_labels': [{'image': img2, 'bboxes': boxes2}]}
>>> labels = {"image": img, "bboxes": boxes, "mix_labels": [{"image": img2, "bboxes": boxes2}]}
>>> augmented_labels = transform._mix_transform(labels)
"""
raise NotImplementedError
@ -456,20 +457,17 @@ class BaseMixTransform:
Examples:
>>> labels = {
... 'texts': [['cat'], ['dog']],
... 'cls': torch.tensor([[0], [1]]),
... 'mix_labels': [{
... 'texts': [['bird'], ['fish']],
... 'cls': torch.tensor([[0], [1]])
... }]
... "texts": [["cat"], ["dog"]],
... "cls": torch.tensor([[0], [1]]),
... "mix_labels": [{"texts": [["bird"], ["fish"]], "cls": torch.tensor([[0], [1]])}],
... }
>>> updated_labels = self._update_label_text(labels)
>>> print(updated_labels['texts'])
>>> print(updated_labels["texts"])
[['cat'], ['dog'], ['bird'], ['fish']]
>>> print(updated_labels['cls'])
>>> print(updated_labels["cls"])
tensor([[0],
[1]])
>>> print(updated_labels['mix_labels'][0]['cls'])
>>> print(updated_labels["mix_labels"][0]["cls"])
tensor([[2],
[3]])
"""
@ -616,9 +614,12 @@ class Mosaic(BaseMixTransform):
Examples:
>>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=3)
>>> labels = {'img': np.random.rand(480, 640, 3), 'mix_labels': [{'img': np.random.rand(480, 640, 3)} for _ in range(2)]}
>>> labels = {
... "img": np.random.rand(480, 640, 3),
... "mix_labels": [{"img": np.random.rand(480, 640, 3)} for _ in range(2)],
... }
>>> result = mosaic._mosaic3(labels)
>>> print(result['img'].shape)
>>> print(result["img"].shape)
(640, 640, 3)
"""
mosaic_labels = []
@ -670,9 +671,10 @@ class Mosaic(BaseMixTransform):
Examples:
>>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=4)
>>> labels = {"img": np.random.rand(480, 640, 3), "mix_labels": [
... {"img": np.random.rand(480, 640, 3)} for _ in range(3)
... ]}
>>> labels = {
... "img": np.random.rand(480, 640, 3),
... "mix_labels": [{"img": np.random.rand(480, 640, 3)} for _ in range(3)],
... }
>>> result = mosaic._mosaic4(labels)
>>> assert result["img"].shape == (1280, 1280, 3)
"""
@ -734,7 +736,7 @@ class Mosaic(BaseMixTransform):
>>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=9)
>>> input_labels = dataset[0]
>>> mosaic_result = mosaic._mosaic9(input_labels)
>>> mosaic_image = mosaic_result['img']
>>> mosaic_image = mosaic_result["img"]
"""
mosaic_labels = []
s = self.imgsz
@ -898,7 +900,7 @@ class MixUp(BaseMixTransform):
Examples:
>>> from ultralytics.data.dataset import YOLODataset
>>> dataset = YOLODataset('path/to/data.yaml')
>>> dataset = YOLODataset("path/to/data.yaml")
>>> mixup = MixUp(dataset, pre_transform=None, p=0.5)
"""
super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)
@ -974,10 +976,10 @@ class RandomPerspective:
Examples:
>>> transform = RandomPerspective(degrees=10, translate=0.1, scale=0.1, shear=10)
>>> image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
>>> labels = {'img': image, 'cls': np.array([0, 1]), 'instances': Instances(...)}
>>> labels = {"img": image, "cls": np.array([0, 1]), "instances": Instances(...)}
>>> result = transform(labels)
>>> transformed_image = result['img']
>>> transformed_instances = result['instances']
>>> transformed_image = result["img"]
>>> transformed_instances = result["instances"]
"""
def __init__(
@ -1209,12 +1211,12 @@ class RandomPerspective:
>>> transform = RandomPerspective()
>>> image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
>>> labels = {
... 'img': image,
... 'cls': np.array([0, 1, 2]),
... 'instances': Instances(bboxes=np.array([[10, 10, 50, 50], [100, 100, 150, 150]]))
... "img": image,
... "cls": np.array([0, 1, 2]),
... "instances": Instances(bboxes=np.array([[10, 10, 50, 50], [100, 100, 150, 150]])),
... }
>>> result = transform(labels)
>>> assert result['img'].shape[:2] == result['resized_shape']
>>> assert result["img"].shape[:2] == result["resized_shape"]
"""
if self.pre_transform and "mosaic_border" not in labels:
labels = self.pre_transform(labels)
@ -1358,9 +1360,9 @@ class RandomHSV:
Examples:
>>> hsv_augmenter = RandomHSV(hgain=0.5, sgain=0.5, vgain=0.5)
>>> labels = {'img': np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)}
>>> labels = {"img": np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)}
>>> hsv_augmenter(labels)
>>> augmented_img = labels['img']
>>> augmented_img = labels["img"]
"""
img = labels["img"]
if self.hgain or self.sgain or self.vgain:
@ -1394,7 +1396,7 @@ class RandomFlip:
__call__: Applies the random flip transformation to an image and its annotations.
Examples:
>>> transform = RandomFlip(p=0.5, direction='horizontal')
>>> transform = RandomFlip(p=0.5, direction="horizontal")
>>> result = transform({"img": image, "instances": instances})
>>> flipped_image = result["img"]
>>> flipped_instances = result["instances"]
@ -1416,8 +1418,8 @@ class RandomFlip:
AssertionError: If direction is not 'horizontal' or 'vertical', or if p is not between 0 and 1.
Examples:
>>> flip = RandomFlip(p=0.5, direction='horizontal')
>>> flip = RandomFlip(p=0.7, direction='vertical', flip_idx=[1, 0, 3, 2, 5, 4])
>>> flip = RandomFlip(p=0.5, direction="horizontal")
>>> flip = RandomFlip(p=0.7, direction="vertical", flip_idx=[1, 0, 3, 2, 5, 4])
"""
assert direction in {"horizontal", "vertical"}, f"Support direction `horizontal` or `vertical`, got {direction}"
assert 0 <= p <= 1.0, f"The probability should be in range [0, 1], but got {p}."
@ -1446,8 +1448,8 @@ class RandomFlip:
'instances' (ultralytics.utils.instance.Instances): Updated instances matching the flipped image.
Examples:
>>> labels = {'img': np.random.rand(640, 640, 3), 'instances': Instances(...)}
>>> random_flip = RandomFlip(p=0.5, direction='horizontal')
>>> labels = {"img": np.random.rand(640, 640, 3), "instances": Instances(...)}
>>> random_flip = RandomFlip(p=0.5, direction="horizontal")
>>> flipped_labels = random_flip(labels)
"""
img = labels["img"]
@ -1493,8 +1495,8 @@ class LetterBox:
Examples:
>>> transform = LetterBox(new_shape=(640, 640))
>>> result = transform(labels)
>>> resized_img = result['img']
>>> updated_instances = result['instances']
>>> resized_img = result["img"]
>>> updated_instances = result["instances"]
"""
def __init__(self, new_shape=(640, 640), auto=False, scaleFill=False, scaleup=True, center=True, stride=32):
@ -1548,9 +1550,9 @@ class LetterBox:
Examples:
>>> letterbox = LetterBox(new_shape=(640, 640))
>>> result = letterbox(labels={'img': np.zeros((480, 640, 3)), 'instances': Instances(...)})
>>> resized_img = result['img']
>>> updated_instances = result['instances']
>>> result = letterbox(labels={"img": np.zeros((480, 640, 3)), "instances": Instances(...)})
>>> resized_img = result["img"]
>>> updated_instances = result["instances"]
"""
if labels is None:
labels = {}
@ -1616,7 +1618,7 @@ class LetterBox:
Examples:
>>> letterbox = LetterBox(new_shape=(640, 640))
>>> labels = {'instances': Instances(...)}
>>> labels = {"instances": Instances(...)}
>>> ratio = (0.5, 0.5)
>>> padw, padh = 10, 20
>>> updated_labels = letterbox._update_labels(labels, ratio, padw, padh)
@ -1643,7 +1645,7 @@ class CopyPaste:
Examples:
>>> copypaste = CopyPaste(p=0.5)
>>> augmented_labels = copypaste(labels)
>>> augmented_image = augmented_labels['img']
>>> augmented_image = augmented_labels["img"]
"""
def __init__(self, p=0.5) -> None:
@ -1680,7 +1682,7 @@ class CopyPaste:
(Dict): Dictionary with augmented image and updated instances under 'img', 'cls', and 'instances' keys.
Examples:
>>> labels = {'img': np.random.rand(640, 640, 3), 'cls': np.array([0, 1, 2]), 'instances': Instances(...)}
>>> labels = {"img": np.random.rand(640, 640, 3), "cls": np.array([0, 1, 2]), "instances": Instances(...)}
>>> augmenter = CopyPaste(p=0.5)
>>> augmented_labels = augmenter(labels)
"""
@ -1765,8 +1767,8 @@ class Albumentations:
Examples:
>>> transform = Albumentations(p=0.5)
>>> augmented = transform(image=image, bboxes=bboxes, class_labels=classes)
>>> augmented_image = augmented['image']
>>> augmented_bboxes = augmented['bboxes']
>>> augmented_image = augmented["image"]
>>> augmented_bboxes = augmented["bboxes"]
Notes:
- Requires Albumentations version 1.0.3 or higher.
@ -1871,7 +1873,7 @@ class Albumentations:
>>> labels = {
... "img": np.random.rand(640, 640, 3),
... "cls": np.array([0, 1]),
... "instances": Instances(bboxes=np.array([[0, 0, 1, 1], [0.5, 0.5, 0.8, 0.8]]))
... "instances": Instances(bboxes=np.array([[0, 0, 1, 1], [0.5, 0.5, 0.8, 0.8]])),
... }
>>> augmented = transform(labels)
>>> assert augmented["img"].shape == (640, 640, 3)
@ -1927,11 +1929,11 @@ class Format:
_format_segments: Converts polygon points to bitmap masks.
Examples:
>>> formatter = Format(bbox_format='xywh', normalize=True, return_mask=True)
>>> formatter = Format(bbox_format="xywh", normalize=True, return_mask=True)
>>> formatted_labels = formatter(labels)
>>> img = formatted_labels['img']
>>> bboxes = formatted_labels['bboxes']
>>> masks = formatted_labels['masks']
>>> img = formatted_labels["img"]
>>> bboxes = formatted_labels["bboxes"]
>>> masks = formatted_labels["masks"]
"""
def __init__(
@ -1975,7 +1977,7 @@ class Format:
bgr (float): The probability to return BGR images.
Examples:
>>> format = Format(bbox_format='xyxy', return_mask=True, return_keypoint=False)
>>> format = Format(bbox_format="xyxy", return_mask=True, return_keypoint=False)
>>> print(format.bbox_format)
xyxy
"""
@ -2013,8 +2015,8 @@ class Format:
- 'batch_idx': Batch index tensor (if batch_idx is True).
Examples:
>>> formatter = Format(bbox_format='xywh', normalize=True, return_mask=True)
>>> labels = {'img': np.random.rand(640, 640, 3), 'cls': np.array([0, 1]), 'instances': Instances(...)}
>>> formatter = Format(bbox_format="xywh", normalize=True, return_mask=True)
>>> labels = {"img": np.random.rand(640, 640, 3), "cls": np.array([0, 1]), "instances": Instances(...)}
>>> formatted_labels = formatter(labels)
>>> print(formatted_labels.keys())
"""
@ -2275,8 +2277,8 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False):
Examples:
>>> from ultralytics.data.dataset import YOLODataset
>>> dataset = YOLODataset(img_path='path/to/images', imgsz=640)
>>> hyp = {'mosaic': 1.0, 'copy_paste': 0.5, 'degrees': 10.0, 'translate': 0.2, 'scale': 0.9}
>>> dataset = YOLODataset(img_path="path/to/images", imgsz=640)
>>> hyp = {"mosaic": 1.0, "copy_paste": 0.5, "degrees": 10.0, "translate": 0.2, "scale": 0.9}
>>> transforms = v8_transforms(dataset, imgsz=640, hyp=hyp)
>>> augmented_data = transforms(dataset[0])
"""
@ -2343,7 +2345,7 @@ def classify_transforms(
Examples:
>>> transforms = classify_transforms(size=224)
>>> img = Image.open('path/to/image.jpg')
>>> img = Image.open("path/to/image.jpg")
>>> transformed_img = transforms(img)
"""
import torchvision.transforms as T # scope for faster 'import ultralytics'
@ -2415,7 +2417,7 @@ def classify_augmentations(
(torchvision.transforms.Compose): A composition of image augmentation transforms.
Examples:
>>> transforms = classify_augmentations(size=224, auto_augment='randaugment')
>>> transforms = classify_augmentations(size=224, auto_augment="randaugment")
>>> augmented_image = transforms(original_image)
"""
# Transforms to apply if Albumentations not installed