174 lines
10 KiB
Python
174 lines
10 KiB
Python
import os
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import random
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import numpy as np
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import cv2
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from tqdm import tqdm
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from PIL import Image
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from torch.utils import data
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from torchvision import transforms
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from .image_proc import preproc
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from .config import Config
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from .utils import path_to_image
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Image.MAX_IMAGE_PIXELS = None # remove DecompressionBombWarning
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config = Config()
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_class_labels_TR_sorted = (
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'Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, '
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'BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, '
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'CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, '
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'Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, '
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'Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, '
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'Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, '
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'KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, '
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'Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, '
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'OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, '
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'RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, '
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'ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, '
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'Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, '
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'TobaccoPipe, Toy, Tractor, TrafficLight, TrafficSign, Trampoline, TransmissionTower, Tree, Tricycle, TrimmerCover, Tripod, Trombone, Truck, Trumpet, Tuba, '
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'UAV, Umbrella, UnevenBars, UtilityPole, VacuumCleaner, Violin, Wakesurfing, Watch, WaterTower, WateringPot, Well, WellLid, Wheel, Wheelchair, WindTurbine, Windmill, WineGlass, WireWhisk, Yacht'
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)
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class_labels_TR_sorted = _class_labels_TR_sorted.split(', ')
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class MyData(data.Dataset):
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def __init__(self, datasets, data_size, is_train=True):
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# data_size is None when using dynamic_size or data_size is manually set to None (for inference in the original size).
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self.is_train = is_train
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self.data_size = data_size
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self.load_all = config.load_all
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self.device = config.device
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valid_extensions = ['.png', '.jpg', '.PNG', '.JPG', '.JPEG']
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if self.is_train and config.auxiliary_classification:
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self.cls_name2id = {_name: _id for _id, _name in enumerate(class_labels_TR_sorted)}
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self.transform_image = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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])
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self.transform_label = transforms.Compose([
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transforms.ToTensor(),
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])
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dataset_root = os.path.join(config.data_root_dir, config.task)
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# datasets can be a list of different datasets for training on combined sets.
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self.image_paths = []
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for dataset in datasets.split('+'):
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image_root = os.path.join(dataset_root, dataset, 'im')
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self.image_paths += [os.path.join(image_root, p) for p in os.listdir(image_root) if any(p.endswith(ext) for ext in valid_extensions)]
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self.label_paths = []
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for p in self.image_paths:
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for ext in valid_extensions:
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## 'im' and 'gt' may need modifying
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p_gt = p.replace('/im/', '/gt/')[:-(len(p.split('.')[-1])+1)] + ext
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file_exists = False
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if os.path.exists(p_gt):
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self.label_paths.append(p_gt)
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file_exists = True
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break
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if not file_exists:
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print('Not exists:', p_gt)
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if len(self.label_paths) != len(self.image_paths):
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set_image_paths = set([os.path.splitext(p.split(os.sep)[-1])[0] for p in self.image_paths])
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set_label_paths = set([os.path.splitext(p.split(os.sep)[-1])[0] for p in self.label_paths])
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print('Path diff:', set_image_paths - set_label_paths)
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raise ValueError(f"There are different numbers of images ({len(self.label_paths)}) and labels ({len(self.image_paths)})")
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if self.load_all:
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self.images_loaded, self.labels_loaded = [], []
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self.class_labels_loaded = []
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# for image_path, label_path in zip(self.image_paths, self.label_paths):
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for image_path, label_path in tqdm(zip(self.image_paths, self.label_paths), total=len(self.image_paths)):
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_image = path_to_image(image_path, size=self.data_size, color_type='rgb')
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_label = path_to_image(label_path, size=self.data_size, color_type='gray')
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self.images_loaded.append(_image)
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self.labels_loaded.append(_label)
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self.class_labels_loaded.append(
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self.cls_name2id[label_path.split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
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)
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def __getitem__(self, index):
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if self.load_all:
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image = self.images_loaded[index]
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label = self.labels_loaded[index]
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class_label = self.class_labels_loaded[index] if self.is_train and config.auxiliary_classification else -1
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else:
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image = path_to_image(self.image_paths[index], size=self.data_size, color_type='rgb')
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label = path_to_image(self.label_paths[index], size=self.data_size, color_type='gray')
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class_label = self.cls_name2id[self.label_paths[index].split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
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# loading image and label
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if self.is_train:
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if config.background_color_synthesis:
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image.putalpha(label)
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array_image = np.array(image)
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array_foreground = array_image[:, :, :3].astype(np.float32)
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array_mask = (array_image[:, :, 3:] / 255).astype(np.float32)
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array_background = np.zeros_like(array_foreground)
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choice = random.random()
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if choice < 0.4:
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# Black/Gray/White backgrounds
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array_background[:, :, :] = random.randint(0, 255)
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elif choice < 0.8:
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# Background color that similar to the foreground object. Hard negative samples.
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foreground_pixel_number = np.sum(array_mask > 0)
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color_foreground_mean = np.mean(array_foreground * array_mask, axis=(0, 1)) * (np.prod(array_foreground.shape[:2]) / foreground_pixel_number)
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color_up_or_down = random.choice((-1, 1))
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# Up or down for 20% range from 255 or 0, respectively.
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color_foreground_mean += (255 - color_foreground_mean if color_up_or_down == 1 else color_foreground_mean) * (random.random() * 0.2) * color_up_or_down
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array_background[:, :, :] = color_foreground_mean
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else:
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# Any color
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for idx_channel in range(3):
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array_background[:, :, idx_channel] = random.randint(0, 255)
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array_foreground_background = array_foreground * array_mask + array_background * (1 - array_mask)
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image = Image.fromarray(array_foreground_background.astype(np.uint8))
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image, label = preproc(image, label, preproc_methods=config.preproc_methods)
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# else:
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# if _label.shape[0] > 2048 or _label.shape[1] > 2048:
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# _image = cv2.resize(_image, (2048, 2048), interpolation=cv2.INTER_LINEAR)
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# _label = cv2.resize(_label, (2048, 2048), interpolation=cv2.INTER_LINEAR)
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# At present, we use fixed sizes in inference, instead of consistent dynamic size with training.
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if self.is_train:
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if config.dynamic_size is None:
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image, label = self.transform_image(image), self.transform_label(label)
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else:
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size_div_32 = (int(image.size[0] // 32 * 32), int(image.size[1] // 32 * 32))
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if image.size != size_div_32:
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image = image.resize(size_div_32)
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label = label.resize(size_div_32)
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image, label = self.transform_image(image), self.transform_label(label)
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if self.is_train:
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return image, label, class_label
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else:
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return image, label, self.label_paths[index]
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def __len__(self):
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return len(self.image_paths)
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def custom_collate_fn(batch):
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if config.dynamic_size:
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dynamic_size = tuple(sorted(config.dynamic_size))
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dynamic_size_batch = (random.randint(dynamic_size[0][0], dynamic_size[0][1]) // 32 * 32, random.randint(dynamic_size[1][0], dynamic_size[1][1]) // 32 * 32) # select a value randomly in the range of [dynamic_size[0/1][0], dynamic_size[0/1][1]].
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data_size = dynamic_size_batch
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else:
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data_size = config.size
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new_batch = []
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transform_image = transforms.Compose([
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transforms.Resize(data_size[::-1]),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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])
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transform_label = transforms.Compose([
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transforms.Resize(data_size[::-1]),
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transforms.ToTensor(),
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])
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for image, label, class_label in batch:
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new_batch.append((transform_image(image), transform_label(label), class_label))
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return data._utils.collate.default_collate(new_batch)
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