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from ..data_aug import cifar_like_image_train_aug, cifar_like_image_test_aug, imagenet_like_image_test_aug, imagenet_like_image_train_aug
from ..ab_dataset import ABDataset
from ..dataset_split import train_val_split, train_val_test_split
from torchvision.datasets import ImageFolder
from typing import Dict, List, Optional
from torchvision.transforms import Compose
from utils.common.others import HiddenPrints
import os
from ..registery import dataset_register
@dataset_register(
name='GTSRB',
classes=[f'{i:05d}' for i in range(42)],
task_type='Image Classification',
object_type='Traffic Sign',
class_aliases=[],
shift_type=None
)
class GTSRB(ABDataset):
def create_dataset(self, root_dir: str, split: str, transform: Optional[Compose],
classes: List[str], ignore_classes: List[str], idx_map: Optional[Dict[int, int]]):
if transform is None:
transform = imagenet_like_image_train_aug() if split == 'train' else imagenet_like_image_test_aug()
self.transform = transform
dataset = ImageFolder(os.path.join(root_dir, 'Final_Training/Images/') if split != 'test' else os.path.join(root_dir, 'Final_Test_ImageFolder/'), transform=transform)
if len(ignore_classes) > 0:
ignore_classes_idx = [classes.index(c) for c in ignore_classes]
dataset.samples = [s for s in dataset.samples if s[1] not in ignore_classes_idx]
if idx_map is not None:
dataset.samples = [(s[0], idx_map[s[1]]) if s[1] in idx_map.keys() else s for s in dataset.samples]
if split != 'test':
dataset = train_val_split(dataset, split)
return dataset
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