FAQ¶
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', 'lq_path']),
dict(type='ImageToTensor', keys=['lq', 'gt'])
]
data = dict(
# train
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_samples_per_gpu=1,
val_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='{}'),