mmdetection/projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-semseg_ade20k.py

_base_ = [
    '_base_/xdecoder-tiny_open-vocab-semseg.py',
    'mmdet::_base_/datasets/ade20k_semantic.py'
]

test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(type='Resize', scale=(2560, 640), 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', 'text'))
]

x_decoder_ade20k_classes = (
    'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed',
    'window', 'grass', 'cabinet', 'sidewalk', 'person', 'earth', 'door',
    'table', 'mountain', 'plant', 'curtain', 'chair', 'car', 'water',
    'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug', 'field',
    'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe', 'lamp', 'tub',
    'rail', 'cushion', 'base', 'box', 'column', 'signboard',
    'chest of drawers', 'counter', 'sand', 'sink', 'skyscraper', 'fireplace',
    'refrigerator', 'grandstand', 'path', 'stairs', 'runway', 'case',
    'pool table', 'pillow', 'screen door', 'stairway', 'river', 'bridge',
    'bookcase', 'blind', 'coffee table', 'toilet', 'flower', 'book', 'hill',
    'bench', 'countertop', 'stove', 'palm', 'kitchen island', 'computer',
    'swivel chair', 'boat', 'bar', 'arcade machine', 'hovel', 'bus', 'towel',
    'light', 'truck', 'tower', 'chandelier', 'awning', 'street lamp', 'booth',
    'tv', 'airplane', 'dirt track', 'clothes', 'pole', 'land', 'bannister',
    'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
    'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
    'pool', 'stool', 'barrel', 'basket', 'falls', 'tent', 'bag', 'minibike',
    'cradle', 'oven', 'ball', 'food', 'step', 'tank', 'trade name',
    'microwave', 'pot', 'animal', 'bicycle', 'lake', 'dishwasher', 'screen',
    'blanket', 'sculpture', 'hood', 'sconce', 'vase', 'traffic light', 'tray',
    'trash can', 'fan', 'pier', 'crt screen', 'plate', 'monitor',
    'bulletin board', 'shower', 'radiator', 'glass', 'clock', 'flag')

val_dataloader = dict(
    dataset=dict(
        metainfo=dict(classes=x_decoder_ade20k_classes),
        return_classes=True,
        use_label_map=False,
        pipeline=test_pipeline))
test_dataloader = val_dataloader