Note

You are reading the documentation for MMEditing 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMEditing 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMEditing 1.0 for more details.

Use intermediate variables in configs

Some intermediate variables are used in the configs files, like train_pipeline/test_pipeline in datasets.

For example, we would like to first define the train_pipeline/test_pipeline and pass them into data. Thus, train_pipeline/test_pipeline are intermediate variable.

...
train_dataset_type = 'SRAnnotationDataset'
val_dataset_type = 'SRFolderDataset'
train_pipeline = [
    dict(
        type='LoadImageFromFile',
        io_backend='disk',
        key='lq',
        flag='unchanged'),
    ...
    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),
    dict(type='ImageToTensor', keys=['lq', 'gt'])
]
test_pipeline = [
    dict(
        type='LoadImageFromFile',
        io_backend='disk',
        key='lq',
        flag='unchanged'),
    ...
    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),
    dict(type='ImageToTensor', keys=['lq', 'gt'])
]

data = dict(
    # train
    train_dataloader = dict(
        samples_per_gpu=16,
        workers_per_gpu=6,
        drop_last=True),
    train=dict(
        type='RepeatDataset',
        times=1000,
        dataset=dict(
            type=train_dataset_type,
            lq_folder='data/DIV2K/DIV2K_train_LR_bicubic/X2_sub',
            gt_folder='data/DIV2K/DIV2K_train_HR_sub',
            ann_file='data/DIV2K/meta_info_DIV2K800sub_GT.txt',
            pipeline=train_pipeline,
            scale=scale)),
    # val
    val_dataloader = dict(samples_per_gpu=1, workers_per_gpu=1),
    val=dict(
        type=val_dataset_type,
        lq_folder='data/val_set5/Set5_bicLRx2',
        gt_folder='data/val_set5/Set5_mod12',
        pipeline=test_pipeline,
        scale=scale,
        filename_tmpl='{}')

empty_cache = True  # empty cache in every iteration.