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executable file
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import sys
from pathlib import Path
from typing import Union, List, Dict, Callable
import torch
from torch.utils.data import Dataset
from datasets import *
PROJECT_ROOT = Path(__file__).absolute().parents[1].absolute()
sys.path.insert(1, str(PROJECT_ROOT))
def get_dataset(dataroot: Union[str, Path], dataset_name: str, split: str, preprocess: Callable, **kwargs) -> Dataset:
"""
This function loads the dataset based on the dataset name provided.
Args:
dataroot (Union[str, Path]): The path to the dataset root directory.
dataset_name (str): The name of the dataset to load.
split (str): The data split to use ('train', 'test', 'query', 'gallery', etc.).
preprocess (Callable): The preprocessing function to apply to the dataset.
Returns:
Dataset: The dataset object for the given dataset name and split.
Raises:
ValueError: If the dataset name is not recognized.
"""
dataset_name = dataset_name.lower()
dataroot = Path(dataroot)
if dataset_name == 'roxford5k':
dataset = ROxfordRParisDataset(dataroot, 'roxford5k', split, preprocess=preprocess)
elif dataset_name == 'rparis6k':
dataset = ROxfordRParisDataset(dataroot, 'rparis6k', split, preprocess=preprocess)
elif dataset_name == 'cub2011':
dataset = CUB(dataroot, split, preprocess=preprocess)
elif dataset_name == 'cub_text':
dataset = CUBTextDataset()
elif dataset_name == 'sop':
dataset = StanfordOnlineProducts(dataroot, split, preprocess=preprocess)
elif dataset_name == 'stanford_cars':
dataset = StanfordCars(dataroot, split, preprocess=preprocess)
elif dataset_name == 'oxford_pets':
dataset = OxfordPets(dataroot, split, preprocess=preprocess)
elif dataset_name == 'oxford_flowers':
dataset = OxfordFlowers(dataroot, split, preprocess=preprocess)
elif dataset_name == 'fgvc_aircraft':
dataset = FGVCAircraft(dataroot, split, preprocess=preprocess)
elif dataset_name == 'dtd':
dataset = DescribableTextures(dataroot, split, preprocess=preprocess)
elif dataset_name == 'eurosat':
dataset = EuroSAT(dataroot, split, preprocess=preprocess)
elif dataset_name == 'food101':
dataset = Food101(dataroot, split, preprocess=preprocess)
elif dataset_name == 'sun397':
dataset = SUN397(dataroot, split, preprocess=preprocess)
elif dataset_name == 'caltech101':
dataset = Caltech101(dataroot, split, preprocess=preprocess)
elif dataset_name == 'ucf101':
dataset = UCF101(dataroot, split, preprocess=preprocess)
elif dataset_name == 'imagenet':
dataset = ImageNet(dataroot, split, preprocess=preprocess)
elif dataset_name == 'coco_text':
dataset = CocoTextDataset(dataroot, split)
elif dataset_name == 'flickr30k_text':
dataset = Flickr30KTextDataset(dataroot, split)
elif dataset_name == 'nocaps_text':
dataset = NoCapsTextDataset(dataroot, split)
elif dataset_name == 'imdb_text':
dataset = IMDBTextDataset(dataroot, split)
elif dataset_name == 'newsgroup_text':
dataset = NewsGroupDataset(split)
elif dataset_name in ['nanoclimatefever', 'nanodbpedia', 'nanofever',
'nanonfcorpus', 'nanonq', 'nanoscidocs',
'nanoscifact']:
dataset = NanoBEIRDataset(split, dataset_name)
elif dataset_name == 'coco':
dataset = CocoDataset(dataroot, split, preprocess=preprocess, return_image=kwargs.get('return_image', True))
elif dataset_name == 'modified_coco':
dataset = ModifiedCocoDataset(dataroot, split, preprocess=preprocess,
return_image=kwargs.get('return_image', True))
elif dataset_name == 'flickr30k':
dataset = Flickr30KDataset(dataroot, split, preprocess=preprocess,
return_image=kwargs.get('return_image', True))
elif dataset_name == 'inaturalist2021':
dataset = INaturalist2021(dataroot, split, preprocess=preprocess)
elif dataset_name == 'places365':
dataset = Places365(dataroot, split, preprocess=preprocess)
else:
raise ValueError(f"Dataset {dataset_name} not recognized")
return dataset
def collate_fn(batch: List[Union[torch.Tensor, None]]) -> torch.Tensor:
"""
Function to filter out None values from a batch when using torch DataLoader.
Args:
batch (List[Union[torch.Tensor, None]]): The input batch containing tensors and possibly None values.
Returns:
torch.Tensor: The batch with None values removed.
"""
batch = list(filter(lambda x: x is not None, batch))
return torch.utils.data.dataloader.default_collate(batch)
# Definition of the dataset splits for retrieval and classification
RETRIEVAL_SPLTS: Dict[str, Dict[str, str]] = {
# IMAGE DATASETS
"cub2011": {'query': 'all', 'gallery': 'all'},
"cub_text": {'query': 'NO_SPLIT', 'gallery': 'NO_SPLIT'},
"roxford5k": {'query': 'query', 'gallery': 'gallery'},
"rparis6k": {'query': 'query', 'gallery': 'gallery'},
"sop": {'query': 'test', 'gallery': 'test'},
"inaturalist2021": {'query': 'train', 'gallery': 'train'},
"places365": {'query': 'val', 'gallery': 'val'},
"caltech101": {'query': 'test', 'gallery': 'train'},
"dtd": {'query': 'test', 'gallery': 'train'},
"eurosat": {'query': 'test', 'gallery': 'train'},
"fgvc_aircraft": {'query': 'test', 'gallery': 'train'},
"food101": {'query': 'test', 'gallery': 'train'},
"imagenet": {'query': 'test', 'gallery': 'train'},
"oxford_flowers": {'query': 'test', 'gallery': 'train'},
"oxford_pets": {'query': 'test', 'gallery': 'train'},
"stanford_cars": {'query': 'test', 'gallery': 'train'},
"sun397": {'query': 'test', 'gallery': 'train'},
"ucf101": {'query': 'test', 'gallery': 'train'},
# TEXT DATASETS
"coco_text": {'query': 'test_query', 'gallery': 'test_gallery'},
"flickr30k_text": {'query': 'val_query', 'gallery': 'val_gallery'},
"imdb_text": {'query': 'query', 'gallery': 'all'},
"nanoclimatefever": {'query': 'query', 'gallery': 'gallery'},
"nanodbpedia": {'query': 'query', 'gallery': 'gallery'},
"nanofever": {'query': 'query', 'gallery': 'gallery'},
"nanonfcorpus": {'query': 'query', 'gallery': 'gallery'},
"nanonq": {'query': 'query', 'gallery': 'gallery'},
"nanoscidocs": {'query': 'query', 'gallery': 'gallery'},
"nanoscifact": {'query': 'query', 'gallery': 'gallery'},
"newsgroup_text": {'query': 'query', 'gallery': 'test'},
"nocaps_text": {'query': 'val_query', 'gallery': 'val_gallery'},
# IMAGE-TEXT DATASETS
"coco": {'query': 'test', 'gallery': 'test'},
"modified_coco": {'query': 'test', 'gallery': 'test'},
"flickr30k": {'query': 'test', 'gallery': 'test'},
}
CLASSIFICATION_SPLTS: Dict[str, Dict[str, str]] = {
"caltech101": {'split': 'test'},
"dtd": {'split': 'test'},
"eurosat": {'split': 'test'},
"fgvc_aircraft": {'split': 'test'},
"food101": {'split': 'test'},
"imagenet": {'split': 'val'},
"oxford_flowers": {'split': 'test'},
"oxford_pets": {'split': 'test'},
"stanford_cars": {'split': 'test'},
"sun397": {'split': 'test'},
"ucf101": {'split': 'test'},
}