update obj365_detection.py

czczup · Feb 6, 2023 · f19aa7b · f19aa7b
1 parent 1441d1e
commit f19aa7b
Showing 1 changed file with 15 additions and 119 deletions.
diff --git a/detection/configs/_base_/datasets/obj365_detection.py b/detection/configs/_base_/datasets/obj365_detection.py
@@ -1,112 +1,17 @@
 # dataset settings
-dataset_type = 'CocoDataset'
-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 ']
-
+dataset_type = 'Objects365V2Dataset'
+data_root = 'data/Objects365/Obj365_v2/'
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-file_client_args = dict(backend='petrel')
 train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadImageFromFile'),
     dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='AutoAugment',
-        policies=[
-            [
-                dict(
-                    type='Resize',
-                    img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                               (576, 1333), (608, 1333), (640, 1333),
-                               (672, 1333), (704, 1333), (736, 1333),
-                               (768, 1333), (800, 1333)],
-                    multiscale_mode='value',
-                    keep_ratio=True)
-            ],
-            [
-                dict(
-                    type='Resize',
-                    # The radio of all image in train dataset < 7
-                    # follow the original impl
-                    img_scale=[(400, 4200), (500, 4200), (600, 4200)],
-                    multiscale_mode='value',
-                    keep_ratio=True),
-                dict(
-                    type='RandomCrop',
-                    crop_type='absolute_range',
-                    crop_size=(384, 600),
-                    allow_negative_crop=False),
-                dict(
-                    type='Resize',
-                    img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                               (576, 1333), (608, 1333), (640, 1333),
-                               (672, 1333), (704, 1333), (736, 1333),
-                               (768, 1333), (800, 1333)],
-                    multiscale_mode='value',
-                    override=True,
-                    keep_ratio=True)
-            ]
-        ]),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
     dict(type='RandomFlip', flip_ratio=0.5),
     dict(type='Normalize', **img_norm_cfg),
     dict(type='Pad', size_divisor=32),
     dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
 ]
 test_pipeline = [
     dict(type='LoadImageFromFile'),
@@ -127,27 +32,18 @@
     samples_per_gpu=2,
     workers_per_gpu=2,
     train=dict(
-        type='ClassBalancedDataset',
-        oversample_thr=1e-3,
-        dataset=dict(
-            type=dataset_type,
-            ann_file='/mnt/petrelfs/share_data/chenzhe1/bigdet/Objects365/zhiyuan_objv2_train.json',  # _Tiny
-            img_prefix='/mnt/petrelfs/share_data/chenzhe1/bigdet/Objects365/',
-            classes=classes,
-            pipeline=train_pipeline
-        )
-    ),
+        type=dataset_type,
+        ann_file=data_root + 'annotations/zhiyuan_objv2_train.json',
+        img_prefix=data_root + 'train/',
+        pipeline=train_pipeline),
     val=dict(
         type=dataset_type,
-        ann_file='/mnt/petrelfs/share_data/chenzhe1/bigdet/Objects365/zhiyuan_objv2_val.json',
-        img_prefix='/mnt/petrelfs/share_data/chenzhe1/bigdet/Objects365/val/',
-        classes=classes,
+        ann_file=data_root + 'annotations/zhiyuan_objv2_val.json',
+        img_prefix=data_root + 'val/',
         pipeline=test_pipeline),
     test=dict(
         type=dataset_type,
-        ann_file='/mnt/petrelfs/share_data/chenzhe1/bigdet/Objects365/zhiyuan_objv2_val.json',
-        img_prefix='/mnt/petrelfs/share_data/chenzhe1/bigdet/Objects365/val/',
-        classes=classes,
-        pipeline=test_pipeline)
-)
-evaluation = dict(metric='bbox')
+        ann_file=data_root + 'annotations/zhiyuan_objv2_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')