mmdetection/configs/_base_/datasets/ade20k_semantic.py

dataset_type = 'ADE20KSegDataset'
data_root = 'data/ADEChallengeData2016/'

# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)

# data_root = 's3://openmmlab/datasets/detection/ADEChallengeData2016/'

# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection/',
#         'data/': 's3://openmmlab/datasets/detection/'
#     }))
backend_args = None

test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='Resize', scale=(2048, 512), keep_ratio=True),
    dict(
        type='LoadAnnotations',
        with_bbox=False,
        with_mask=False,
        with_seg=True,
        reduce_zero_label=True),
    dict(
        type='PackDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape'))
]

val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        data_prefix=dict(
            img_path='images/validation',
            seg_map_path='annotations/validation'),
        pipeline=test_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(type='SemSegMetric', iou_metrics=['mIoU'])
test_evaluator = val_evaluator