备注

您正在阅读 MMClassification 0.x 版本的文档。MMClassification 0.x 会在 2022 年末被切换为次要分支。建议您升级到 MMClassification 1.0 版本，体验更多新特性和新功能。请查阅 MMClassification 1.0 的安装教程、迁移教程以及更新日志。

Data Transformations

In MMClassification, the data preparation and the dataset is decomposed. The datasets only define how to get samples’ basic information from the file system. These basic information includes the ground-truth label and raw images data / the paths of images.

To prepare the inputs data, we need to do some transformations on these basic information. These transformations includes loading, preprocessing and formatting. And a series of data transformations makes up a data pipeline. Therefore, you can find the a pipeline argument in the configs of dataset, for example:

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='RandomResizedCrop', size=224),
    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='ToTensor', keys=['gt_label']),
    dict(type='Collect', keys=['img', 'gt_label'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', size=256),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img'])
]

data = dict(
    train=dict(..., pipeline=train_pipeline),
    val=dict(..., pipeline=test_pipeline),
    test=dict(..., pipeline=test_pipeline),
)

Every item of a pipeline list is one of the following data transformations class. And if you want to add a custom data transformation class, the tutorial Custom Data Pipelines will help you.

Loading

LoadImageFromFile

Preprocessing and Augmentation

CenterCrop

Lighting

Normalize

Pad

Resize

RandomCrop

RandomErasing

RandomFlip

RandomGrayscale

RandomResizedCrop

ColorJitter

Composed Augmentation

Composed augmentation is a kind of methods which compose a series of data augmentation transformations, such as AutoAugment and RandAugment.

In composed augmentation, we need to specify several data transformations or several groups of data transformations (The policies argument) as the random sampling space. These data transformations are chosen from the below table. In addition, we provide some preset policies in this folder.

Formatting

Collect

ImageToTensor

ToNumpy

ToPIL

ToTensor

Transpose