You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.
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,
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.
Composed augmentation is a kind of methods which compose a series of data
augmentation transformations, such as
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.