注意

您正在阅读 MMSelfSup 0.x 版本的文档，而 MMSelfSup 0.x 版本将会在 2022 年末开始逐步停止维护。我们建议您及时升级到 MMSelfSup 1.0.0rc 版本，享受由 OpenMMLab 2.0 带来的更多新特性和更佳的性能表现。阅读 MMSelfSup 1.0.0rc 的发版日志, 代码和文档获取更多信息。

mmselfsup.datasets.base 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod

from mmcv.utils import build_from_cfg
from torch.utils.data import Dataset
from torchvision.transforms import Compose

from .builder import PIPELINES, build_datasource


[文档]class BaseDataset(Dataset, metaclass=ABCMeta):
    """Base dataset class.

    The base dataset can be inherited by different algorithm's datasets. After
    `__init__`, the data source and pipeline will be built. Besides, the
    algorithm specific dataset implements different operations after obtaining
    images from data sources.

    Args:
        data_source (dict): Data source defined in
            `mmselfsup.datasets.data_sources`.
        pipeline (list[dict]): A list of dict, where each element represents
            an operation defined in `mmselfsup.datasets.pipelines`.
        prefetch (bool, optional): Whether to prefetch data. Defaults to False.
    """

    def __init__(self, data_source, pipeline, prefetch=False):
        warnings.warn('The dataset part will be refactored, it will soon '
                      'support `dict` in pipelines to save more information, '
                      'the same as the pipeline in `MMDet`.')
        self.data_source = build_datasource(data_source)
        pipeline = [build_from_cfg(p, PIPELINES) for p in pipeline]
        self.pipeline = Compose(pipeline)
        self.prefetch = prefetch
        self.CLASSES = self.data_source.CLASSES

    def __len__(self):
        return len(self.data_source)

    @abstractmethod
    def __getitem__(self, idx):
        pass

    @abstractmethod
    def evaluate(self, results, logger=None, **kwargs):
        pass