数据转换 (需要更新)

数据转换管道的设计

遵循典型约定，我们使用 Dataset 和 DataLoader 来加载数据，并使用多个工作进程。 Dataset 返回一个包含数据项的字典，这些数据项对应于模型前向方法的参数。

数据转换管道和数据集是分离的。通常，数据集定义如何处理注释，而数据转换管道定义准备数据字典的所有步骤。管道由一系列数据转换组成。每个操作都接受一个字典作为输入，并输出一个字典供下一个转换使用。

我们在下图中展示了一个经典的管道。蓝色块是管道操作。随着管道的进行，每个操作符可以向结果字典添加新键（标记为绿色）或更新现有键（标记为橙色）。

这是一个 Faster R-CNN 的管道示例。

train_pipeline = [  # Training data processing pipeline
    dict(type='LoadImageFromFile', backend_args=backend_args),  # First pipeline to load images from file path
    dict(
        type='LoadAnnotations',  # Second pipeline to load annotations for current image
        with_bbox=True),  # Whether to use bounding box, True for detection
    dict(
        type='Resize',  # Pipeline that resize the images and their annotations
        scale=(1333, 800),  # The largest scale of image
        keep_ratio=True  # Whether to keep the ratio between height and width
        ),
    dict(
        type='RandomFlip',  # Augmentation pipeline that flip the images and their annotations
        prob=0.5),  # The probability to flip
    dict(type='PackDetInputs')  # Pipeline that formats the annotation data and decides which keys in the data should be packed into data_samples
]
test_pipeline = [  # Testing data processing pipeline
    dict(type='LoadImageFromFile', backend_args=backend_args),  # First pipeline to load images from file path
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),  # Pipeline that resize the images
    dict(
        type='PackDetInputs',  # Pipeline that formats the annotation data and decides which keys in the data should be packed into data_samples
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]