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# ---------------------------------------------------------------------------------------------------
# CLIP-DINOiser
# authors: Monika Wysoczanska, Warsaw University of Technology
from mmseg.datasets import DATASETS, CustomDataset
@DATASETS.register_module(force=True)
class COCOStuffDataset(CustomDataset):
"""COCO-Stuff dataset.
In segmentation map annotation for COCO-Stuff, Train-IDs of the 10k version
are from 1 to 171, where 0 is the ignore index, and Train-ID of COCO Stuff
164k is from 0 to 170, where 255 is the ignore index. So, they are all 171
semantic categories. ``reduce_zero_label`` is set to True and False for the
10k and 164k versions, respectively. The ``img_suffix`` is fixed to '.jpg',
and ``seg_map_suffix`` is fixed to '.png'.
"""
CLASSES = (
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner',
'blanket', 'branch', 'bridge', 'building-other', 'bush', 'cabinet',
'cage', 'cardboard', 'carpet', 'ceiling-other', 'ceiling-tile',
'cloth', 'clothes', 'clouds', 'counter', 'cupboard', 'curtain',
'desk-stuff', 'dirt', 'door-stuff', 'fence', 'floor-marble',
'floor-other', 'floor-stone', 'floor-tile', 'floor-wood',
'flower', 'fog', 'food-other', 'fruit', 'furniture-other', 'grass',
'gravel', 'ground-other', 'hill', 'house', 'leaves', 'light', 'mat',
'metal', 'mirror-stuff', 'moss', 'mountain', 'mud', 'napkin', 'net',
'paper', 'pavement', 'pillow', 'plant-other', 'plastic', 'platform',
'playingfield', 'railing', 'railroad', 'river', 'road', 'rock', 'roof',
'rug', 'salad', 'sand', 'sea', 'shelf', 'sky-other', 'skyscraper',
'snow', 'solid-other', 'stairs', 'stone', 'straw', 'structural-other',
'table', 'tent', 'textile-other', 'towel', 'tree', 'vegetable',
'wall-brick', 'wall-concrete', 'wall-other', 'wall-panel',
'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'waterdrops',
'window-blind', 'window-other', 'wood')
PALETTE = [[0, 192, 64], [0, 192, 64], [0, 64, 96], [128, 192, 192],
[0, 64, 64], [0, 192, 224], [0, 192, 192], [128, 192, 64],
[0, 192, 96], [128, 192, 64], [128, 32, 192], [0, 0, 224],
[0, 0, 64], [0, 160, 192], [128, 0, 96], [128, 0, 192],
[0, 32, 192], [128, 128, 224], [0, 0, 192], [128, 160, 192],
[128, 128, 0], [128, 0, 32], [128, 32, 0], [128, 0, 128],
[64, 128, 32], [0, 160, 0], [0, 0, 0], [192, 128, 160],
[0, 32, 0], [0, 128, 128], [64, 128, 160], [128, 160, 0],
[0, 128, 0], [192, 128, 32], [128, 96, 128], [0, 0, 128],
[64, 0, 32], [0, 224, 128], [128, 0, 0], [192, 0, 160],
[0, 96, 128], [128, 128, 128], [64, 0, 160], [128, 224, 128],
[128, 128, 64], [192, 0, 32], [128, 96, 0], [128, 0, 192],
[0, 128, 32], [64, 224, 0], [0, 0, 64], [128, 128, 160],
[64, 96, 0], [0, 128, 192], [0, 128, 160], [192, 224, 0],
[0, 128, 64], [128, 128, 32], [192, 32, 128], [0, 64, 192],
[0, 0, 32], [64, 160, 128], [128, 64, 64], [128, 0, 160],
[64, 32, 128], [128, 192, 192], [0, 0, 160], [192, 160, 128],
[128, 192, 0], [128, 0, 96], [192, 32, 0], [128, 64, 128],
[64, 128, 96], [64, 160, 0], [0, 64, 0], [192, 128, 224],
[64, 32, 0], [0, 192, 128], [64, 128, 224], [192, 160, 0],
[0, 192, 0], [192, 128, 96], [192, 96, 128], [0, 64, 128],
[64, 0, 96], [64, 224, 128], [128, 64, 0], [192, 0, 224],
[64, 96, 128], [128, 192, 128], [64, 0, 224], [192, 224, 128],
[128, 192, 64], [192, 0, 96], [192, 96, 0], [128, 64, 192],
[0, 128, 96], [0, 224, 0], [64, 64, 64], [128, 128, 224],
[0, 96, 0], [64, 192, 192], [0, 128, 224], [128, 224, 0],
[64, 192, 64], [128, 128, 96], [128, 32, 128], [64, 0, 192],
[0, 64, 96], [0, 160, 128], [192, 0, 64], [128, 64, 224],
[0, 32, 128], [192, 128, 192], [0, 64, 224], [128, 160, 128],
[192, 128, 0], [128, 64, 32], [128, 32, 64], [192, 0, 128],
[64, 192, 32], [0, 160, 64], [64, 0, 0], [192, 192, 160],
[0, 32, 64], [64, 128, 128], [64, 192, 160], [128, 160, 64],
[64, 128, 0], [192, 192, 32], [128, 96, 192], [64, 0, 128],
[64, 64, 32], [0, 224, 192], [192, 0, 0], [192, 64, 160],
[0, 96, 192], [192, 128, 128], [64, 64, 160], [128, 224, 192],
[192, 128, 64], [192, 64, 32], [128, 96, 64], [192, 0, 192],
[0, 192, 32], [64, 224, 64], [64, 0, 64], [128, 192, 160],
[64, 96, 64], [64, 128, 192], [0, 192, 160], [192, 224, 64],
[64, 128, 64], [128, 192, 32], [192, 32, 192], [64, 64, 192],
[0, 64, 32], [64, 160, 192], [192, 64, 64], [128, 64, 160],
[64, 32, 192], [192, 192, 192], [0, 64, 160], [192, 160, 192],
[192, 192, 0], [128, 64, 96], [192, 32, 64], [192, 64, 128],
[64, 192, 96], [64, 160, 64], [64, 64, 0]]
def __init__(self, **kwargs):
super(COCOStuffDataset, self).__init__(
img_suffix='.jpg', seg_map_suffix='_labelTrainIds.png', **kwargs) |