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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| import collections | |
| from copy import deepcopy | |
| from .augment import LetterBox | |
| class MixAndRectDataset: | |
| """ | |
| A dataset class that applies mosaic and mixup transformations as well as rectangular training. | |
| Attributes: | |
| dataset: The base dataset. | |
| imgsz: The size of the images in the dataset. | |
| """ | |
| def __init__(self, dataset): | |
| """ | |
| Args: | |
| dataset (BaseDataset): The base dataset to apply transformations to. | |
| """ | |
| self.dataset = dataset | |
| self.imgsz = dataset.imgsz | |
| def __len__(self): | |
| """Returns the number of items in the dataset.""" | |
| return len(self.dataset) | |
| def __getitem__(self, index): | |
| """ | |
| Applies mosaic, mixup and rectangular training transformations to an item in the dataset. | |
| Args: | |
| index (int): Index of the item in the dataset. | |
| Returns: | |
| (dict): A dictionary containing the transformed item data. | |
| """ | |
| labels = deepcopy(self.dataset[index]) | |
| for transform in self.dataset.transforms.tolist(): | |
| # Mosaic and mixup | |
| if hasattr(transform, 'get_indexes'): | |
| indexes = transform.get_indexes(self.dataset) | |
| if not isinstance(indexes, collections.abc.Sequence): | |
| indexes = [indexes] | |
| labels['mix_labels'] = [deepcopy(self.dataset[index]) for index in indexes] | |
| if self.dataset.rect and isinstance(transform, LetterBox): | |
| transform.new_shape = self.dataset.batch_shapes[self.dataset.batch[index]] | |
| labels = transform(labels) | |
| if 'mix_labels' in labels: | |
| labels.pop('mix_labels') | |
| return labels | |