-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathmetadata.py
170 lines (155 loc) · 7.36 KB
/
metadata.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
from collections import ChainMap
# Detectron imports
from detectron2.data import MetadataCatalog
# Useful Dicts for OpenImages Conversion
OPEN_IMAGES_TO_COCO = {'Person': 'person',
'Bicycle': 'bicycle',
'Car': 'car',
'Motorcycle': 'motorcycle',
'Airplane': 'airplane',
'Bus': 'bus',
'Train': 'train',
'Truck': 'truck',
'Boat': 'boat',
'Traffic light': 'traffic light',
'Fire hydrant': 'fire hydrant',
'Stop sign': 'stop sign',
'Parking meter': 'parking meter',
'Bench': 'bench',
'Bird': 'bird',
'Cat': 'cat',
'Dog': 'dog',
'Horse': 'horse',
'Sheep': 'sheep',
'Elephant': 'cow',
'Cattle': 'elephant',
'Bear': 'bear',
'Zebra': 'zebra',
'Giraffe': 'giraffe',
'Backpack': 'backpack',
'Umbrella': 'umbrella',
'Handbag': 'handbag',
'Tie': 'tie',
'Suitcase': 'suitcase',
'Flying disc': 'frisbee',
'Ski': 'skis',
'Snowboard': 'snowboard',
'Ball': 'sports ball',
'Kite': 'kite',
'Baseball bat': 'baseball bat',
'Baseball glove': 'baseball glove',
'Skateboard': 'skateboard',
'Surfboard': 'surfboard',
'Tennis racket': 'tennis racket',
'Bottle': 'bottle',
'Wine glass': 'wine glass',
'Coffee cup': 'cup',
'Fork': 'fork',
'Knife': 'knife',
'Spoon': 'spoon',
'Bowl': 'bowl',
'Banana': 'banana',
'Apple': 'apple',
'Sandwich': 'sandwich',
'Orange': 'orange',
'Broccoli': 'broccoli',
'Carrot': 'carrot',
'Hot dog': 'hot dog',
'Pizza': 'pizza',
'Doughnut': 'donut',
'Cake': 'cake',
'Chair': 'chair',
'Couch': 'couch',
'Houseplant': 'potted plant',
'Bed': 'bed',
'Table': 'dining table',
'Toilet': 'toilet',
'Television': 'tv',
'Laptop': 'laptop',
'Computer mouse': 'mouse',
'Remote control': 'remote',
'Computer keyboard': 'keyboard',
'Mobile phone': 'cell phone',
'Microwave oven': 'microwave',
'Oven': 'oven',
'Toaster': 'toaster',
'Sink': 'sink',
'Refrigerator': 'refrigerator',
'Book': 'book',
'Clock': 'clock',
'Vase': 'vase',
'Scissors': 'scissors',
'Teddy bear': 'teddy bear',
'Hair dryer': 'hair drier',
'Toothbrush': 'toothbrush'}
# Construct COCO metadata
COCO_THING_CLASSES = MetadataCatalog.get('coco_2017_train').thing_classes
COCO_THING_DATASET_ID_TO_CONTIGUOUS_ID = MetadataCatalog.get(
'coco_2017_train').thing_dataset_id_to_contiguous_id
# Construct OpenImages metadata
OPENIMAGES_THING_DATASET_ID_TO_CONTIGUOUS_ID = dict(
ChainMap(*[{i + 1: i} for i in range(len(COCO_THING_CLASSES))]))
# MAP COCO to OpenImages contiguous id to be used for inference on OpenImages for models
# trained on COCO.
COCO_TO_OPENIMAGES_CONTIGUOUS_ID = dict(ChainMap(
*[{COCO_THING_CLASSES.index(openimages_thing_class): COCO_THING_CLASSES.index(openimages_thing_class)} for openimages_thing_class in
COCO_THING_CLASSES]))
# import ipdb; ipdb.set_trace()
# Construct VOC metadata
VOC_THING_CLASSES = ['person',
'bird',
'cat',
'cow',
'dog',
'horse',
'sheep',
'airplane',
'bicycle',
'boat',
'bus',
'car',
'motorcycle',
'train',
'bottle',
'chair',
'dining table',
'potted plant',
'couch',
'tv',
]
VOC_ID_THING_CLASSES = [
'person', 'dog', 'horse', 'sheep', 'motorcycle', 'train', 'dining table', 'potted plant', 'couch', 'tv'
]
VOC_OOD_THING_CLASSES = [
'bird', 'cat', 'cow' , 'airplane', 'bicycle', 'boat', 'bus', 'car', 'bottle', 'chair'
]
# COCO_OOD_THING_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']
# VOC_THING_DATASET_ID_TO_CONTIGUOUS_ID = dict(
# ChainMap(*[{i + 1: i} for i in range(len(VOC_THING_CLASSES))]))
# import ipdb; ipdb.set_trace()
VOC_THING_DATASET_ID_TO_CONTIGUOUS_ID_in_domain = dict(
ChainMap(*[{i + 1: i} for i in range(10)]))
VOC_THING_DATASET_ID_TO_CONTIGUOUS_ID = dict(
ChainMap(*[{i + 1: i} for i in range(20)]))
# MAP COCO to VOC contiguous id to be used for inference on VOC for models
# trained on COCO.
COCO_TO_VOC_CONTIGUOUS_ID = dict(ChainMap(
*[{COCO_THING_CLASSES.index(voc_thing_class): VOC_THING_CLASSES.index(voc_thing_class)} for voc_thing_class in
VOC_THING_CLASSES]))
# import ipdb; ipdb.set_trace()
BDD_THING_CLASSES = ['pedestrian', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle', 'traffic light', 'traffic sign']#, "OOD"]
BDD_THING_DATASET_ID_TO_CONTIGUOUS_ID = dict(
ChainMap(*[{i + 1: i} for i in range(10)]))