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from ..data_aug import imagenet_like_image_train_aug, imagenet_like_image_test_aug |
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from ..ab_dataset import ABDataset |
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from ..dataset_split import train_val_split |
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from torchvision.datasets import ImageFolder |
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import os |
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from typing import Dict, List, Optional |
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from torchvision.transforms import Compose |
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from ..registery import dataset_register |
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with open(os.path.join(os.path.dirname(__file__), 'imagenet_classes.txt'), 'r') as f: |
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classes = [line.split(' ')[2].strip() for line in f.readlines()] |
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assert len(classes) == 1000 |
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@dataset_register( |
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name='ImageNet-A', |
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classes=classes, |
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task_type='Image Classification', |
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object_type='Generic Object', |
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class_aliases=[], |
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shift_type={ |
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'ImageNet': 'Adversarially Filtered Shifts' |
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} |
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) |
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class ImageNetA(ABDataset): |
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def create_dataset(self, root_dir: str, split: str, transform: Optional[Compose], |
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classes: List[str], ignore_classes: List[str], idx_map: Optional[Dict[int, int]]): |
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if transform is None: |
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transform = imagenet_like_image_train_aug() if split == 'train' else imagenet_like_image_test_aug() |
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self.transform = transform |
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root_dir = os.path.join(root_dir, 'train' if split != 'test' else 'val') |
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dataset = ImageFolder(root_dir, transform=transform) |
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if len(ignore_classes) > 0: |
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ignore_classes_idx = [classes.index(c) for c in ignore_classes] |
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dataset.samples = [s for s in dataset.samples if s[1] not in ignore_classes_idx] |
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if idx_map is not None: |
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dataset.samples = [(s[0], idx_map[s[1]]) if s[1] in idx_map.keys() else s for s in dataset.samples] |
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if split != 'test': |
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dataset = train_val_split(dataset, split) |
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return dataset |
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