services/training_service/engine/solver/_solver.py

import torch
import torch.nn as nn

from datetime import datetime
from pathlib import Path
from typing import Dict
import atexit

from ..misc import dist_utils
from ..core import BaseConfig


def to(m: nn.Module, device: str):
    if m is None:
        return None
    return m.to(device)


def remove_module_prefix(state_dict):
    new_state_dict = {}
    for k, v in state_dict.items():
        if k.startswith('module.'):
            new_state_dict[k[7:]] = v
        else:
            new_state_dict[k] = v
    return new_state_dict


class BaseSolver(object):
    def __init__(self, cfg: BaseConfig) -> None:
        self.cfg = cfg
        self.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
        ]
    def _setup(self):
        """Avoid instantiating unnecessary classes"""
        cfg = self.cfg
        if cfg.device:
            device = torch.device(cfg.device)
        else:
            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

        self.model = cfg.model

        # NOTE: Must load_tuning_state before EMA instance building
        if self.cfg.tuning:
            print(f'Tuning checkpoint from {self.cfg.tuning}')
            self.load_tuning_state(self.cfg.tuning)

        self.model = dist_utils.warp_model(
            self.model.to(device), sync_bn=cfg.sync_bn, find_unused_parameters=cfg.find_unused_parameters
        )

        self.criterion = self.to(cfg.criterion, device)
        self.postprocessor = self.to(cfg.postprocessor, device)

        self.ema = self.to(cfg.ema, device)
        self.scaler = cfg.scaler

        self.device = device
        self.last_epoch = self.cfg.last_epoch

        self.output_dir = Path(cfg.output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)
        self.writer = cfg.writer

        if self.writer:
            atexit.register(self.writer.close)
            if dist_utils.is_main_process():
                self.writer.add_text('config', '{:s}'.format(cfg.__repr__()), 0)

    def cleanup(self):
        if self.writer:
            atexit.register(self.writer.close)

    def train(self):
        self._setup()
        self.optimizer = self.cfg.optimizer
        self.lr_scheduler = self.cfg.lr_scheduler
        self.lr_warmup_scheduler = self.cfg.lr_warmup_scheduler

        self.train_dataloader = dist_utils.warp_loader(
            self.cfg.train_dataloader, shuffle=self.cfg.train_dataloader.shuffle
        )
        self.val_dataloader = dist_utils.warp_loader(
            self.cfg.val_dataloader, shuffle=self.cfg.val_dataloader.shuffle
        )

        self.evaluator = self.cfg.evaluator

        # NOTE: Instantiating order
        if self.cfg.resume:
            print(f'Resume checkpoint from {self.cfg.resume}')
            self.load_resume_state(self.cfg.resume)

    def eval(self):
        self._setup()

        self.val_dataloader = dist_utils.warp_loader(
            self.cfg.val_dataloader, shuffle=self.cfg.val_dataloader.shuffle
        )

        self.evaluator = self.cfg.evaluator

        if self.cfg.resume:
            print(f'Resume checkpoint from {self.cfg.resume}')
            self.load_resume_state(self.cfg.resume)

    def to(self, module, device):
        return module.to(device) if hasattr(module, 'to') else module

    def state_dict(self):
        """State dict, train/eval"""
        state = {}
        state['date'] = datetime.now().isoformat()

        # For resume
        state['last_epoch'] = self.last_epoch

        for k, v in self.__dict__.items():
            if hasattr(v, 'state_dict'):
                v = dist_utils.de_parallel(v)
                state[k] = v.state_dict()

        return state

    def load_state_dict(self, state):
        """Load state dict, train/eval"""
        if 'last_epoch' in state:
            self.last_epoch = state['last_epoch']
            print('Load last_epoch')

        for k, v in self.__dict__.items():
            if hasattr(v, 'load_state_dict') and k in state:
                v = dist_utils.de_parallel(v)
                v.load_state_dict(state[k])
                print(f'Load {k}.state_dict')

            if hasattr(v, 'load_state_dict') and k not in state:
                if k == 'ema':
                    model = getattr(self, 'model', None)
                    if model is not None:
                        ema = dist_utils.de_parallel(v)
                        model_state_dict = remove_module_prefix(model.state_dict())
                        ema.load_state_dict({'module': model_state_dict})
                        print(f'Load {k}.state_dict from model.state_dict')
                else:
                    print(f'Not load {k}.state_dict')

    def load_resume_state(self, path: str):
        """Load resume"""
        if path.startswith('http'):
            state = torch.hub.load_state_dict_from_url(path, map_location='cpu')
        else:
            state = torch.load(path, map_location='cpu')

        # state['model'] = remove_module_prefix(state['model'])
        self.load_state_dict(state)

    def load_tuning_state(self, path: str):
        """Load model for tuning and adjust mismatched head parameters"""
        if path.startswith('http'):
            state = torch.hub.load_state_dict_from_url(path, map_location='cpu')
        else:
            state = torch.load(path, map_location='cpu')

        module = dist_utils.de_parallel(self.model)

        # Load the appropriate state dict
        if 'ema' in state:
            pretrain_state_dict = state['ema']['module']
        else:
            pretrain_state_dict = state['model']

        # Adjust head parameters between datasets
        try:
            adjusted_state_dict = self._adjust_head_parameters(module.state_dict(), pretrain_state_dict)
            stat, infos = self._matched_state(module.state_dict(), adjusted_state_dict)
        except Exception:
            stat, infos = self._matched_state(module.state_dict(), pretrain_state_dict)

        module.load_state_dict(stat, strict=False)
        print(f'Load model.state_dict, {infos}')

    @staticmethod
    def _matched_state(state: Dict[str, torch.Tensor], params: Dict[str, torch.Tensor]):
        missed_list = []
        unmatched_list = []
        matched_state = {}
        for k, v in state.items():
            if k in params:
                if v.shape == params[k].shape:
                    matched_state[k] = params[k]
                else:
                    unmatched_list.append(k)
            else:
                missed_list.append(k)

        return matched_state, {'missed': missed_list, 'unmatched': unmatched_list}

    def _adjust_head_parameters(self, cur_state_dict, pretrain_state_dict):
        """Adjust head parameters between datasets."""
        # List of parameters to adjust
        if pretrain_state_dict['decoder.denoising_class_embed.weight'].size() != \
                cur_state_dict['decoder.denoising_class_embed.weight'].size():
            del pretrain_state_dict['decoder.denoising_class_embed.weight']

        head_param_names = [
            'decoder.enc_score_head.weight',
            'decoder.enc_score_head.bias'
        ]
        for i in range(8):
            head_param_names.append(f'decoder.dec_score_head.{i}.weight')
            head_param_names.append(f'decoder.dec_score_head.{i}.bias')

        adjusted_params = []

        for param_name in head_param_names:
            if param_name in cur_state_dict and param_name in pretrain_state_dict:
                cur_tensor = cur_state_dict[param_name]
                pretrain_tensor = pretrain_state_dict[param_name]
                adjusted_tensor = self.map_class_weights(cur_tensor, pretrain_tensor)
                if adjusted_tensor is not None:
                    pretrain_state_dict[param_name] = adjusted_tensor
                    adjusted_params.append(param_name)
                else:
                    print(f"Cannot adjust parameter '{param_name}' due to size mismatch.")

        return pretrain_state_dict

    def map_class_weights(self, cur_tensor, pretrain_tensor):
        """Map class weights from pretrain model to current model based on class IDs."""
        if pretrain_tensor.size() == cur_tensor.size():
            return pretrain_tensor

        adjusted_tensor = cur_tensor.clone()
        adjusted_tensor.requires_grad = False

        if pretrain_tensor.size() > cur_tensor.size():
            for coco_id, obj_id in enumerate(self.obj365_ids):
                adjusted_tensor[coco_id] = pretrain_tensor[obj_id+1]
        else:
            for coco_id, obj_id in enumerate(self.obj365_ids):
                adjusted_tensor[obj_id+1] = pretrain_tensor[coco_id]

        return adjusted_tensor

    def fit(self):
        raise NotImplementedError('')

    def val(self):
        raise NotImplementedError('')

# 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']


# obj365_classes = [
#     (0, 'Person'),
#     (1, 'Sneakers'),
#     (2, 'Chair'),
#     (3, 'Other Shoes'),
#     (4, 'Hat'),
#     (5, 'Car'),
#     (6, 'Lamp'),
#     (7, 'Glasses'),
#     (8, 'Bottle'),
#     (9, 'Desk'),
#     (10, 'Cup'),
#     (11, 'Street Lights'),
#     (12, 'Cabinet/shelf'),
#     (13, 'Handbag/Satchel'),
#     (14, 'Bracelet'),
#     (15, 'Plate'),
#     (16, 'Picture/Frame'),
#     (17, 'Helmet'),
#     (18, 'Book'),
#     (19, 'Gloves'),
#     (20, 'Storage box'),
#     (21, 'Boat'),
#     (22, 'Leather Shoes'),
#     (23, 'Flower'),
#     (24, 'Bench'),
#     (25, 'Potted Plant'),
#     (26, 'Bowl/Basin'),
#     (27, 'Flag'),
#     (28, 'Pillow'),
#     (29, 'Boots'),
#     (30, 'Vase'),
#     (31, 'Microphone'),
#     (32, 'Necklace'),
#     (33, 'Ring'),
#     (34, 'SUV'),
#     (35, 'Wine Glass'),
#     (36, 'Belt'),
#     (37, 'Monitor/TV'),
#     (38, 'Backpack'),
#     (39, 'Umbrella'),
#     (40, 'Traffic Light'),
#     (41, 'Speaker'),
#     (42, 'Watch'),
#     (43, 'Tie'),
#     (44, 'Trash bin Can'),
#     (45, 'Slippers'),
#     (46, 'Bicycle'),
#     (47, 'Stool'),
#     (48, 'Barrel/bucket'),
#     (49, 'Van'),
#     (50, 'Couch'),
#     (51, 'Sandals'),
#     (52, 'Basket'),
#     (53, 'Drum'),
#     (54, 'Pen/Pencil'),
#     (55, 'Bus'),
#     (56, 'Wild Bird'),
#     (57, 'High Heels'),
#     (58, 'Motorcycle'),
#     (59, 'Guitar'),
#     (60, 'Carpet'),
#     (61, 'Cell Phone'),
#     (62, 'Bread'),
#     (63, 'Camera'),
#     (64, 'Canned'),
#     (65, 'Truck'),
#     (66, 'Traffic cone'),
#     (67, 'Cymbal'),
#     (68, 'Lifesaver'),
#     (69, 'Towel'),
#     (70, 'Stuffed Toy'),
#     (71, 'Candle'),
#     (72, 'Sailboat'),
#     (73, 'Laptop'),
#     (74, 'Awning'),
#     (75, 'Bed'),
#     (76, 'Faucet'),
#     (77, 'Tent'),
#     (78, 'Horse'),
#     (79, 'Mirror'),
#     (80, 'Power outlet'),
#     (81, 'Sink'),
#     (82, 'Apple'),
#     (83, 'Air Conditioner'),
#     (84, 'Knife'),
#     (85, 'Hockey Stick'),
#     (86, 'Paddle'),
#     (87, 'Pickup Truck'),
#     (88, 'Fork'),
#     (89, 'Traffic Sign'),
#     (90, 'Balloon'),
#     (91, 'Tripod'),
#     (92, 'Dog'),
#     (93, 'Spoon'),
#     (94, 'Clock'),
#     (95, 'Pot'),
#     (96, 'Cow'),
#     (97, 'Cake'),
#     (98, 'Dining Table'),
#     (99, 'Sheep'),
#     (100, 'Hanger'),
#     (101, 'Blackboard/Whiteboard'),
#     (102, 'Napkin'),
#     (103, 'Other Fish'),
#     (104, 'Orange/Tangerine'),
#     (105, 'Toiletry'),
#     (106, 'Keyboard'),
#     (107, 'Tomato'),
#     (108, 'Lantern'),
#     (109, 'Machinery Vehicle'),
#     (110, 'Fan'),
#     (111, 'Green Vegetables'),
#     (112, 'Banana'),
#     (113, 'Baseball Glove'),
#     (114, 'Airplane'),
#     (115, 'Mouse'),
#     (116, 'Train'),
#     (117, 'Pumpkin'),
#     (118, 'Soccer'),
#     (119, 'Skiboard'),
#     (120, 'Luggage'),
#     (121, 'Nightstand'),
#     (122, 'Tea pot'),
#     (123, 'Telephone'),
#     (124, 'Trolley'),
#     (125, 'Head Phone'),
#     (126, 'Sports Car'),
#     (127, 'Stop Sign'),
#     (128, 'Dessert'),
#     (129, 'Scooter'),
#     (130, 'Stroller'),
#     (131, 'Crane'),
#     (132, 'Remote'),
#     (133, 'Refrigerator'),
#     (134, 'Oven'),
#     (135, 'Lemon'),
#     (136, 'Duck'),
#     (137, 'Baseball Bat'),
#     (138, 'Surveillance Camera'),
#     (139, 'Cat'),
#     (140, 'Jug'),
#     (141, 'Broccoli'),
#     (142, 'Piano'),
#     (143, 'Pizza'),
#     (144, 'Elephant'),
#     (145, 'Skateboard'),
#     (146, 'Surfboard'),
#     (147, 'Gun'),
#     (148, 'Skating and Skiing Shoes'),
#     (149, 'Gas Stove'),
#     (150, 'Donut'),
#     (151, 'Bow Tie'),
#     (152, 'Carrot'),
#     (153, 'Toilet'),
#     (154, 'Kite'),
#     (155, 'Strawberry'),
#     (156, 'Other Balls'),
#     (157, 'Shovel'),
#     (158, 'Pepper'),
#     (159, 'Computer Box'),
#     (160, 'Toilet Paper'),
#     (161, 'Cleaning Products'),
#     (162, 'Chopsticks'),
#     (163, 'Microwave'),
#     (164, 'Pigeon'),
#     (165, 'Baseball'),
#     (166, 'Cutting/chopping Board'),
#     (167, 'Coffee Table'),
#     (168, 'Side Table'),
#     (169, 'Scissors'),
#     (170, 'Marker'),
#     (171, 'Pie'),
#     (172, 'Ladder'),
#     (173, 'Snowboard'),
#     (174, 'Cookies'),
#     (175, 'Radiator'),
#     (176, 'Fire Hydrant'),
#     (177, 'Basketball'),
#     (178, 'Zebra'),
#     (179, 'Grape'),
#     (180, 'Giraffe'),
#     (181, 'Potato'),
#     (182, 'Sausage'),
#     (183, 'Tricycle'),
#     (184, 'Violin'),
#     (185, 'Egg'),
#     (186, 'Fire Extinguisher'),
#     (187, 'Candy'),
#     (188, 'Fire Truck'),
#     (189, 'Billiards'),
#     (190, 'Converter'),
#     (191, 'Bathtub'),
#     (192, 'Wheelchair'),
#     (193, 'Golf Club'),
#     (194, 'Briefcase'),
#     (195, 'Cucumber'),
#     (196, 'Cigar/Cigarette'),
#     (197, 'Paint Brush'),
#     (198, 'Pear'),
#     (199, 'Heavy Truck'),
#     (200, 'Hamburger'),
#     (201, 'Extractor'),
#     (202, 'Extension Cord'),
#     (203, 'Tong'),
#     (204, 'Tennis Racket'),
#     (205, 'Folder'),
#     (206, 'American Football'),
#     (207, 'Earphone'),
#     (208, 'Mask'),
#     (209, 'Kettle'),
#     (210, 'Tennis'),
#     (211, 'Ship'),
#     (212, 'Swing'),
#     (213, 'Coffee Machine'),
#     (214, 'Slide'),
#     (215, 'Carriage'),
#     (216, 'Onion'),
#     (217, 'Green Beans'),
#     (218, 'Projector'),
#     (219, 'Frisbee'),
#     (220, 'Washing Machine/Drying Machine'),
#     (221, 'Chicken'),
#     (222, 'Printer'),
#     (223, 'Watermelon'),
#     (224, 'Saxophone'),
#     (225, 'Tissue'),
#     (226, 'Toothbrush'),
#     (227, 'Ice Cream'),
#     (228, 'Hot Air Balloon'),
#     (229, 'Cello'),
#     (230, 'French Fries'),
#     (231, 'Scale'),
#     (232, 'Trophy'),
#     (233, 'Cabbage'),
#     (234, 'Hot Dog'),
#     (235, 'Blender'),
#     (236, 'Peach'),
#     (237, 'Rice'),
#     (238, 'Wallet/Purse'),
#     (239, 'Volleyball'),
#     (240, 'Deer'),
#     (241, 'Goose'),
#     (242, 'Tape'),
#     (243, 'Tablet'),
#     (244, 'Cosmetics'),
#     (245, 'Trumpet'),
#     (246, 'Pineapple'),
#     (247, 'Golf Ball'),
#     (248, 'Ambulance'),
#     (249, 'Parking Meter'),
#     (250, 'Mango'),
#     (251, 'Key'),
#     (252, 'Hurdle'),
#     (253, 'Fishing Rod'),
#     (254, 'Medal'),
#     (255, 'Flute'),
#     (256, 'Brush'),
#     (257, 'Penguin'),
#     (258, 'Megaphone'),
#     (259, 'Corn'),
#     (260, 'Lettuce'),
#     (261, 'Garlic'),
#     (262, 'Swan'),
#     (263, 'Helicopter'),
#     (264, 'Green Onion'),
#     (265, 'Sandwich'),
#     (266, 'Nuts'),
#     (267, 'Speed Limit Sign'),
#     (268, 'Induction Cooker'),
#     (269, 'Broom'),
#     (270, 'Trombone'),
#     (271, 'Plum'),
#     (272, 'Rickshaw'),
#     (273, 'Goldfish'),
#     (274, 'Kiwi Fruit'),
#     (275, 'Router/Modem'),
#     (276, 'Poker Card'),
#     (277, 'Toaster'),
#     (278, 'Shrimp'),
#     (279, 'Sushi'),
#     (280, 'Cheese'),
#     (281, 'Notepaper'),
#     (282, 'Cherry'),
#     (283, 'Pliers'),
#     (284, 'CD'),
#     (285, 'Pasta'),
#     (286, 'Hammer'),
#     (287, 'Cue'),
#     (288, 'Avocado'),
#     (289, 'Hami Melon'),
#     (290, 'Flask'),
#     (291, 'Mushroom'),
#     (292, 'Screwdriver'),
#     (293, 'Soap'),
#     (294, 'Recorder'),
#     (295, 'Bear'),
#     (296, 'Eggplant'),
#     (297, 'Board Eraser'),
#     (298, 'Coconut'),
#     (299, 'Tape Measure/Ruler'),
#     (300, 'Pig'),
#     (301, 'Showerhead'),
#     (302, 'Globe'),
#     (303, 'Chips'),
#     (304, 'Steak'),
#     (305, 'Crosswalk Sign'),
#     (306, 'Stapler'),
#     (307, 'Camel'),
#     (308, 'Formula 1'),
#     (309, 'Pomegranate'),
#     (310, 'Dishwasher'),
#     (311, 'Crab'),
#     (312, 'Hoverboard'),
#     (313, 'Meatball'),
#     (314, 'Rice Cooker'),
#     (315, 'Tuba'),
#     (316, 'Calculator'),
#     (317, 'Papaya'),
#     (318, 'Antelope'),
#     (319, 'Parrot'),
#     (320, 'Seal'),
#     (321, 'Butterfly'),
#     (322, 'Dumbbell'),
#     (323, 'Donkey'),
#     (324, 'Lion'),
#     (325, 'Urinal'),
#     (326, 'Dolphin'),
#     (327, 'Electric Drill'),
#     (328, 'Hair Dryer'),
#     (329, 'Egg Tart'),
#     (330, 'Jellyfish'),
#     (331, 'Treadmill'),
#     (332, 'Lighter'),
#     (333, 'Grapefruit'),
#     (334, 'Game Board'),
#     (335, 'Mop'),
#     (336, 'Radish'),
#     (337, 'Baozi'),
#     (338, 'Target'),
#     (339, 'French'),
#     (340, 'Spring Rolls'),
#     (341, 'Monkey'),
#     (342, 'Rabbit'),
#     (343, 'Pencil Case'),
#     (344, 'Yak'),
#     (345, 'Red Cabbage'),
#     (346, 'Binoculars'),
#     (347, 'Asparagus'),
#     (348, 'Barbell'),
#     (349, 'Scallop'),
#     (350, 'Noodles'),
#     (351, 'Comb'),
#     (352, 'Dumpling'),
#     (353, 'Oyster'),
#     (354, 'Table Tennis Paddle'),
#     (355, 'Cosmetics Brush/Eyeliner Pencil'),
#     (356, 'Chainsaw'),
#     (357, 'Eraser'),
#     (358, 'Lobster'),
#     (359, 'Durian'),
#     (360, 'Okra'),
#     (361, 'Lipstick'),
#     (362, 'Cosmetics Mirror'),
#     (363, 'Curling'),
#     (364, 'Table Tennis')
# ]