dataset_type = 'RAVIRDataset'
data_root = 'data/ravir'
img_scale = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='Resize', scale=img_scale, keep_ratio=False),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='PackSegInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=img_scale, keep_ratio=False),
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs')
]
train_dataloader = dict(
    batch_size=16,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='InfiniteSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='train.txt',
        data_prefix=dict(img_path='images/', seg_map_path='masks/'),
        pipeline=train_pipeline))
val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='val.txt',
        data_prefix=dict(img_path='images/', seg_map_path='masks/'),
        pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])
test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])
