ultralytics 8.2.62 add Explorer CLI model and data args (#14581)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
GitHub
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commit
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8 changed files with 153 additions and 127 deletions
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@ -159,11 +159,11 @@ class Compose:
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tolist: Converts the list of transforms to a standard Python list.
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Examples:
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>>> transforms = [RandomFlip(), RandomRotate(30), RandomCrop((224, 224))]
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>>> transforms = [RandomFlip(), RandomPerspective(30)]
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>>> compose = Compose(transforms)
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>>> transformed_data = compose(data)
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>>> compose.append(Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
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>>> compose.insert(0, Resize((256, 256)))
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>>> compose.append(CenterCrop((224, 224)))
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>>> compose.insert(0, RandomFlip())
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"""
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def __init__(self, transforms):
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@ -174,8 +174,8 @@ class Compose:
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transforms (List[Callable]): A list of callable transform objects to be applied sequentially.
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Examples:
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>>> from ultralytics.data.augment import Compose, Resize, RandomFlip
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>>> transforms = [Resize(640), RandomFlip()]
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>>> from ultralytics.data.augment import Compose, RandomHSV, RandomFlip
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>>> transforms = [RandomHSV(), RandomFlip()]
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>>> compose = Compose(transforms)
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"""
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self.transforms = transforms if isinstance(transforms, list) else [transforms]
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@ -209,7 +209,7 @@ class Compose:
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transform (BaseTransform): The transformation to be added to the composition.
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Examples:
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>>> compose = Compose([RandomFlip(), RandomRotate()])
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>>> compose = Compose([RandomFlip(), RandomPerspective()])
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>>> compose.append(RandomHSV())
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"""
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self.transforms.append(transform)
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@ -232,7 +232,7 @@ class Compose:
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def __getitem__(self, index: Union[list, int]) -> "Compose":
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"""
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Retrieve a specific transform or a set of transforms using indexing.
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Retrieves a specific transform or a set of transforms using indexing.
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Args:
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index (int | List[int]): Index or list of indices of the transforms to retrieve.
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@ -244,10 +244,10 @@ class Compose:
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AssertionError: If the index is not of type int or list.
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Examples:
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>>> transforms = [RandomFlip(), RandomRotate(10), RandomHSV(0.5, 0.5, 0.5)]
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>>> transforms = [RandomFlip(), RandomPerspective(10), RandomHSV(0.5, 0.5, 0.5)]
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>>> compose = Compose(transforms)
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>>> single_transform = compose[1] # Returns a Compose object with only RandomRotate
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>>> multiple_transforms = compose[0:2] # Returns a Compose object with RandomFlip and RandomRotate
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>>> single_transform = compose[1] # Returns a Compose object with only RandomPerspective
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>>> multiple_transforms = compose[0:2] # Returns a Compose object with RandomFlip and RandomPerspective
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"""
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assert isinstance(index, (int, list)), f"The indices should be either list or int type but got {type(index)}"
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index = [index] if isinstance(index, int) else index
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@ -288,7 +288,7 @@ class Compose:
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(List): A list containing all the transform objects in the Compose instance.
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Examples:
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>>> transforms = [RandomFlip(), RandomRotate(10), RandomCrop()]
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>>> transforms = [RandomFlip(), RandomPerspective(10), CenterCrop()]
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>>> compose = Compose(transforms)
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>>> transform_list = compose.tolist()
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>>> print(len(transform_list))
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@ -304,12 +304,12 @@ class Compose:
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(str): A string representation of the Compose object, including the list of transforms.
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Examples:
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>>> transforms = [RandomFlip(), RandomAffine(degrees=10, translate=0.1, scale=0.1)]
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>>> transforms = [RandomFlip(), RandomPerspective(degrees=10, translate=0.1, scale=0.1)]
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>>> compose = Compose(transforms)
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>>> print(compose)
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Compose([
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RandomFlip(),
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RandomAffine(degrees=10, translate=0.1, scale=0.1)
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RandomPerspective(degrees=10, translate=0.1, scale=0.1)
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])
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"""
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return f"{self.__class__.__name__}({', '.join([f'{t}' for t in self.transforms])})"
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@ -353,12 +353,12 @@ class BaseMixTransform:
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Args:
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dataset (Any): The dataset object containing images and labels for mixing.
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pre_transform (Callable | None): Optional transform to apply before mixing. If None, no pre-transform is applied.
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pre_transform (Callable | None): Optional transform to apply before mixing.
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p (float): Probability of applying the mix transformation. Should be in the range [0.0, 1.0].
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Examples:
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>>> dataset = YOLODataset("path/to/data")
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>>> pre_transform = Compose([RandomFlip(), RandomRotate()])
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>>> pre_transform = Compose([RandomFlip(), RandomPerspective()])
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>>> mix_transform = BaseMixTransform(dataset, pre_transform, p=0.5)
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"""
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self.dataset = dataset
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@ -420,7 +420,7 @@ class BaseMixTransform:
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(Dict): The modified labels dictionary with augmented data after applying the mix transform.
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Examples:
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>>> transform = MixUpTransform(dataset)
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>>> transform = BaseMixTransform(dataset)
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>>> labels = {'image': img, 'bboxes': boxes, 'mix_labels': [{'image': img2, 'bboxes': boxes2}]}
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>>> augmented_labels = transform._mix_transform(labels)
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"""
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@ -662,13 +662,12 @@ class Mosaic(BaseMixTransform):
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updates the corresponding labels for each image in the mosaic.
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Args:
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labels (Dict): A dictionary containing image data and labels for the base image (index 0) and
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three additional images (indices 1-3) in the 'mix_labels' key.
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labels (Dict): A dictionary containing image data and labels for the base image (index 0) and three
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additional images (indices 1-3) in the 'mix_labels' key.
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Returns:
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(Dict): A dictionary containing the mosaic image and updated labels. The 'img' key contains the
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mosaic image as a numpy array, and other keys contain the combined and adjusted labels for
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all four images.
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(Dict): A dictionary containing the mosaic image and updated labels. The 'img' key contains the mosaic
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image as a numpy array, and other keys contain the combined and adjusted labels for all four images.
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Examples:
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>>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=4)
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@ -963,7 +962,7 @@ class RandomPerspective:
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shear (float): Maximum shear angle in degrees.
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perspective (float): Perspective distortion factor.
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border (Tuple[int, int]): Mosaic border size as (x, y).
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pre_transform (callable): Optional transform to apply before the random perspective.
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pre_transform (Callable | None): Optional transform to apply before the random perspective.
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Methods:
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affine_transform: Applies affine transformations to the input image.
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@ -988,9 +987,8 @@ class RandomPerspective:
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"""
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Initializes RandomPerspective object with transformation parameters.
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This class implements random perspective and affine transformations on images and corresponding
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bounding boxes, segments, and keypoints. Transformations include rotation, translation, scaling,
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and shearing.
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This class implements random perspective and affine transformations on images and corresponding bounding boxes,
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segments, and keypoints. Transformations include rotation, translation, scaling, and shearing.
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Args:
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degrees (float): Degree range for random rotations.
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@ -999,8 +997,8 @@ class RandomPerspective:
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shear (float): Shear intensity (angle in degrees).
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perspective (float): Perspective distortion factor.
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border (Tuple[int, int]): Tuple specifying mosaic border (top/bottom, left/right).
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pre_transform (Callable | None): Function/transform to apply to the image before starting the
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random transformation.
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pre_transform (Callable | None): Function/transform to apply to the image before starting the random
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transformation.
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Examples:
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>>> transform = RandomPerspective(degrees=10.0, translate=0.1, scale=0.5, shear=5.0)
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@ -1121,8 +1119,8 @@ class RandomPerspective:
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the transformed segments. It clips the transformed segments to fit within the new bounding boxes.
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Args:
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segments (np.ndarray): Input segments with shape (N, M, 2), where N is the number of segments and M
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is the number of points in each segment.
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segments (np.ndarray): Input segments with shape (N, M, 2), where N is the number of segments and M is the
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number of points in each segment.
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M (np.ndarray): Affine transformation matrix with shape (3, 3).
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Returns:
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@ -1203,10 +1201,10 @@ class RandomPerspective:
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Returns:
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(Dict): Transformed labels dictionary containing:
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'img' (ndarray): The transformed image.
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'cls' (ndarray): Updated class labels.
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'instances' (Instances): Updated object instances.
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'resized_shape' (Tuple[int, int]): New image shape after transformation.
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- 'img' (np.ndarray): The transformed image.
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- 'cls' (np.ndarray): Updated class labels.
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- 'instances' (Instances): Updated object instances.
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- 'resized_shape' (Tuple[int, int]): New image shape after transformation.
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Examples:
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>>> transform = RandomPerspective()
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@ -1271,9 +1269,9 @@ class RandomPerspective:
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been overly distorted or reduced by the augmentation process.
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Args:
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box1 (numpy.ndarray): Original boxes before augmentation, shape (4, n) where n is the
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box1 (numpy.ndarray): Original boxes before augmentation, shape (4, N) where n is the
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number of boxes. Format is [x1, y1, x2, y2] in absolute coordinates.
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box2 (numpy.ndarray): Augmented boxes after transformation, shape (4, n). Format is
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box2 (numpy.ndarray): Augmented boxes after transformation, shape (4, N). Format is
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[x1, y1, x2, y2] in absolute coordinates.
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wh_thr (float): Width and height threshold in pixels. Boxes smaller than this in either
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dimension are rejected.
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@ -1411,9 +1409,8 @@ class RandomFlip:
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It also updates any instances (bounding boxes, keypoints, etc.) accordingly.
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Args:
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p (float): The probability of applying the flip. Must be between 0 and 1. Default is 0.5.
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p (float): The probability of applying the flip. Must be between 0 and 1.
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direction (str): The direction to apply the flip. Must be 'horizontal' or 'vertical'.
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Default is 'horizontal'.
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flip_idx (List[int] | None): Index mapping for flipping keypoints, if any.
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Raises:
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@ -1538,15 +1535,15 @@ class LetterBox:
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"""
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Resizes and pads an image for object detection, instance segmentation, or pose estimation tasks.
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This method applies letterboxing to the input image, which involves resizing the image while maintaining its aspect
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ratio and adding padding to fit the new shape. It also updates any associated labels accordingly.
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This method applies letterboxing to the input image, which involves resizing the image while maintaining its
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aspect ratio and adding padding to fit the new shape. It also updates any associated labels accordingly.
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Args:
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labels (dict | None): A dictionary containing image data and associated labels. If None, an empty dict is used.
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image (numpy.ndarray | None): The input image as a numpy array. If None, the image is taken from 'labels'.
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labels (Dict | None): A dictionary containing image data and associated labels, or empty dict if None.
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image (np.ndarray | None): The input image as a numpy array. If None, the image is taken from 'labels'.
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Returns:
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(dict | tuple): If 'labels' is provided, returns an updated dictionary with the resized and padded image,
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(Dict | Tuple): If 'labels' is provided, returns an updated dictionary with the resized and padded image,
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updated labels, and additional metadata. If 'labels' is empty, returns a tuple containing the resized
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and padded image, and a tuple of (ratio, (left_pad, top_pad)).
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@ -1675,17 +1672,16 @@ class CopyPaste:
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Applies Copy-Paste augmentation to an image and its instances.
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Args:
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labels (dict): A dictionary containing:
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- 'img' (numpy.ndarray): The image to augment.
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- 'cls' (numpy.ndarray): Class labels for the instances.
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labels (Dict): A dictionary containing:
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- 'img' (np.ndarray): The image to augment.
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- 'cls' (np.ndarray): Class labels for the instances.
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- 'instances' (ultralytics.engine.results.Instances): Object containing bounding boxes, segments, etc.
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Returns:
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(dict): Dictionary with augmented image and updated instances under 'img', 'cls', and 'instances' keys.
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(Dict): Dictionary with augmented image and updated instances under 'img', 'cls', and 'instances' keys.
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Examples:
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>>> labels = {'img': np.random.rand(640, 640, 3), 'cls': np.array([0, 1, 2]),
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... 'instances': Instances(...)}
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>>> labels = {'img': np.random.rand(640, 640, 3), 'cls': np.array([0, 1, 2]), 'instances': Instances(...)}
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>>> augmenter = CopyPaste(p=0.5)
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>>> augmented_labels = augmenter(labels)
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"""
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@ -1874,8 +1870,12 @@ class Albumentations:
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Examples:
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>>> transform = Albumentations(p=0.5)
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>>> augmented = transform({"img": np.random.rand(640, 640, 3), "cls": np.array([0, 1]),
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... "instances": Instances(bboxes=np.array([[0, 0, 1, 1], [0.5, 0.5, 0.8, 0.8]]))})
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>>> labels = {
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... "img": np.random.rand(640, 640, 3),
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... "cls": np.array([0, 1]),
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... "instances": Instances(bboxes=np.array([[0, 0, 1, 1], [0.5, 0.5, 0.8, 0.8]]))
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... }
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>>> augmented = transform(labels)
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>>> assert augmented["img"].shape == (640, 640, 3)
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Notes:
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@ -1974,6 +1974,7 @@ class Format:
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mask_ratio (int): Downsample ratio for masks.
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mask_overlap (bool): Whether masks can overlap.
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batch_idx (bool): Whether to keep batch indexes.
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bgr (float): The probability to return BGR images.
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Examples:
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>>> format = Format(bbox_format='xyxy', return_mask=True, return_keypoint=False)
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@ -1994,9 +1995,9 @@ class Format:
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"""
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Formats image annotations for object detection, instance segmentation, and pose estimation tasks.
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This method standardizes the image and instance annotations to be used by the `collate_fn` in PyTorch DataLoader.
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It processes the input labels dictionary, converting annotations to the specified format and applying
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normalization if required.
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This method standardizes the image and instance annotations to be used by the `collate_fn` in PyTorch
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DataLoader. It processes the input labels dictionary, converting annotations to the specified format and
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applying normalization if required.
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Args:
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labels (Dict): A dictionary containing image and annotation data with the following keys:
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@ -2069,7 +2070,7 @@ class Format:
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5. Converts the Numpy array to a PyTorch tensor.
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Args:
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img (ndarray): Input image as a Numpy array with shape (H, W, C) or (H, W).
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img (np.ndarray): Input image as a Numpy array with shape (H, W, C) or (H, W).
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Returns:
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(torch.Tensor): Formatted image as a PyTorch tensor with shape (C, H, W).
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@ -2130,11 +2131,11 @@ class RandomLoadText:
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to reflect the sampled texts and can optionally pad the text list to a fixed length.
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Attributes:
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prompt_format (str): Format string for text prompts. Default is '{}'.
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neg_samples (Tuple[int, int]): Range for randomly sampling negative texts. Default is (80, 80).
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max_samples (int): Maximum number of different text samples in one image. Default is 80.
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padding (bool): Whether to pad texts to max_samples. Default is False.
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padding_value (str): The text used for padding when padding is True. Default is "".
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prompt_format (str): Format string for text prompts.
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neg_samples (Tuple[int, int]): Range for randomly sampling negative texts.
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max_samples (int): Maximum number of different text samples in one image.
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padding (bool): Whether to pad texts to max_samples.
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padding_value (str): The text used for padding when padding is True.
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Methods:
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__call__: Processes the input labels and returns updated classes and texts.
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@ -2268,15 +2269,15 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False):
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Args:
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dataset (Dataset): The dataset object containing image data and annotations.
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imgsz (int): The target image size for resizing.
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hyp (dict): A dictionary of hyperparameters controlling various aspects of the transformations.
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hyp (Dict): A dictionary of hyperparameters controlling various aspects of the transformations.
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stretch (bool): If True, applies stretching to the image. If False, uses LetterBox resizing.
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Returns:
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(Compose): A composition of image transformations to be applied to the dataset.
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Examples:
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>>> from ultralytics.data.dataset import Dataset
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>>> dataset = Dataset(img_path='path/to/images', imgsz=640)
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>>> from ultralytics.data.dataset import YOLODataset
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>>> dataset = YOLODataset(img_path='path/to/images', imgsz=640)
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>>> hyp = {'mosaic': 1.0, 'copy_paste': 0.5, 'degrees': 10.0, 'translate': 0.2, 'scale': 0.9}
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>>> transforms = v8_transforms(dataset, imgsz=640, hyp=hyp)
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>>> augmented_data = transforms(dataset[0])
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@ -2332,15 +2333,12 @@ def classify_transforms(
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center cropping, conversion to tensor, and normalization.
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Args:
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size (int | tuple): The target size for the transformed image. If an int, it defines the
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shortest edge. If a tuple, it defines (height, width).
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mean (tuple): Mean values for each RGB channel used in normalization. Defaults to
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DEFAULT_MEAN.
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size (int | tuple): The target size for the transformed image. If an int, it defines the shortest edge. If a
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tuple, it defines (height, width).
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mean (tuple): Mean values for each RGB channel used in normalization.
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std (tuple): Standard deviation values for each RGB channel used in normalization.
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Defaults to DEFAULT_STD.
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interpolation (int): Interpolation method for resizing. Defaults to Image.BILINEAR.
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crop_fraction (float): Fraction of the image to be cropped. Defaults to
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DEFAULT_CROP_FRACTION.
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interpolation (int): Interpolation method for resizing.
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crop_fraction (float): Fraction of the image to be cropped.
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Returns:
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(torchvision.transforms.Compose): A composition of torchvision transforms.
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@ -2482,7 +2480,7 @@ def classify_augmentations(
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# NOTE: keep this class for backward compatibility
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class ClassifyLetterBox:
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"""
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YOLOv8 LetterBox class for image preprocessing in classification tasks.
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A class for resizing and padding images for classification tasks.
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This class is designed to be part of a transformation pipeline, e.g., T.Compose([LetterBox(size), ToTensor()]).
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It resizes and pads images to a specified size while maintaining the original aspect ratio.
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@ -2512,8 +2510,8 @@ class ClassifyLetterBox:
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pads images to a specified size while maintaining the original aspect ratio.
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Args:
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size (Union[int, Tuple[int, int]]): Target size for the letterboxed image. If int, a square image of
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(size, size) is created. If tuple, it should be (height, width).
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size (int | Tuple[int, int]): Target size for the letterboxed image. If an int, a square image of
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(size, size) is created. If a tuple, it should be (height, width).
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auto (bool): If True, automatically calculates the short side based on stride. Default is False.
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stride (int): The stride value, used when 'auto' is True. Default is 32.
|
||||
|
||||
|
|
|
|||
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Add table
Add a link
Reference in a new issue