File size: 7,602 Bytes
066effd |
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 |
# ------------------------------------------------------------------------
# LW-DETR
# Copyright (c) 2024 Baidu. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
"""Utils to load object365 pretrain."""
# obj365_classes = [
# 'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses',
# 'Bottle', 'Desk', 'Cup', 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel',
# 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book', 'Gloves', 'Storage box',
# 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
# 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass',
# 'Belt', 'Moniter/TV', 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch',
# 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', 'Stool', 'Barrel/bucket', 'Van',
# 'Couch', 'Sandals', 'Bakset', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird', 'High Heels',
# 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
# 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat',
# 'Laptop', 'Awning', 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet',
# 'Sink', 'Apple', 'Air Conditioner', 'Knife', 'Hockey Stick', 'Paddle', 'Pickup Truck',
# 'Fork', 'Traffic Sign', 'Ballon', 'Tripod', 'Dog', 'Spoon', 'Clock', 'Pot', 'Cow',
# 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin',
# 'Other Fish', 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern',
# 'Machinery Vehicle', 'Fan', 'Green Vegetables', 'Banana', 'Baseball Glove',
# 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', 'Luggage', 'Nightstand',
# 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign', 'Dessert',
# 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck',
# 'Baseball Bat', 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza',
# 'Elephant', 'Skateboard', 'Surfboard', 'Gun', 'Skating and Skiing shoes', 'Gas stove',
# 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry', 'Other Balls', 'Shovel',
# 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks', 'Microwave',
# 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
# 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball',
# 'Zebra', 'Grape', 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg',
# 'Fire Extinguisher', 'Candy', 'Fire Truck', 'Billards', 'Converter', 'Bathtub',
# 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette ', 'Paint Brush',
# 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extention Cord', 'Tong',
# 'Tennis Racket', 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle',
# 'Tennis', 'Ship', 'Swing', 'Coffee Machine', 'Slide', 'Carriage', 'Onion',
# 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine', 'Chicken',
# 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream',
# 'Hotair ballon', 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog',
# 'Blender', 'Peach', 'Rice', 'Wallet/Purse', 'Volleyball', 'Deer', 'Goose', 'Tape',
# 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball', 'Ambulance', 'Parking meter',
# 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
# 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion',
# 'Sandwich', 'Nuts', 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone',
# 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit', 'Router/modem', 'Poker Card', 'Toaster',
# 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD', 'Pasta', 'Hammer',
# 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroon', 'Screwdriver', 'Soap', 'Recorder',
# 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measur/ Ruler', 'Pig',
# 'Showerhead', 'Globe', 'Chips', 'Steak', 'Crosswalk Sign', 'Stapler', 'Campel',
# 'Formula 1 ', 'Pomegranate', 'Dishwasher', 'Crab', 'Hoverboard', 'Meat ball',
# 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
# 'Buttefly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill',
# 'Hair Dryer', 'Egg tart', 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit',
# 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French', 'Spring Rolls', 'Monkey',
# 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
# 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Teniis paddle',
# 'Cosmetics Brush/Eyeliner Pencil', 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra',
# 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis '
# ]
# coco_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
# 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
# 'stop sign', 'parking meter', 'bench', 'wild bird', 'cat', 'dog',
# 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
# 'backpack', 'umbrella', 'handbag/satchel', 'tie', 'luggage', 'frisbee',
# 'skating and skiing shoes', 'snowboard', 'baseball', 'kite', 'baseball bat',
# 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
# 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl/basin',
# 'banana', 'apple', 'sandwich', 'orange/tangerine', 'broccoli', 'carrot',
# 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
# 'potted plant', 'bed', 'dinning table', 'toilet', 'moniter/tv', 'laptop',
# 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
# 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
# 'vase', 'scissors', 'stuffed toy', 'hair dryer', 'toothbrush']
def get_coco_pretrain_from_obj365(cur_tensor, pretrain_tensor):
"""Get coco weights from obj365 pretrained model."""
if pretrain_tensor.size() == cur_tensor.size():
return pretrain_tensor
cur_tensor.requires_grad = False
coco_ids = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49,
50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74,
75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90
]
obj365_ids = [
0, 46, 5, 58, 114, 55, 116, 65, 21, 40, 176, 127, 249, 24, 56, 139, 92, 78, 99, 96,
144, 295, 178, 180, 38, 39, 13, 43, 120, 219, 148, 173, 165, 154, 137, 113, 145, 146,
204, 8, 35, 10, 88, 84, 93, 26, 112, 82, 265, 104, 141, 152, 234, 143, 150, 97, 2,
50, 25, 75, 98, 153, 37, 73, 115, 132, 106, 61, 163, 134, 277, 81, 133, 18, 94, 30,
169, 70, 328, 226
]
for coco_id, obj_id in zip(coco_ids, obj365_ids):
cur_tensor[coco_id] = pretrain_tensor[obj_id + 1]
return cur_tensor
|