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| import logging | |
| import os | |
| from typing import List, Tuple, TypeVar | |
| import pandas as pd | |
| import torch | |
| from PIL import Image | |
| from torch.utils.data import Dataset, DataLoader | |
| from torch import Tensor | |
| from torchvision import transforms | |
| from pathlib import Path | |
| PathLike = TypeVar("PathLike", str, Path, None) | |
| log = logging.getLogger(__name__) | |
| PROJECT_DIR = Path(os.path.realpath(__file__)).parent.parent | |
| DATADIR = Path("<PATH_TO_DATA>/ChestXray-NIHCC/images") | |
| # can be found at https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345 | |
| def build_dataloaders( | |
| data_dir: str=DATADIR, | |
| img_size: int=128, | |
| batch_size: int=16, | |
| num_workers: int=1, | |
| ) -> Tuple[List, List, List]: | |
| """ | |
| Build dataloaders for the CXR14 dataset. | |
| """ | |
| train_ds = CXR14Dataset(data_dir, PROJECT_DIR / 'data' / 'train_split.csv', img_size) | |
| val_ds = CXR14Dataset(data_dir, PROJECT_DIR / 'data' / 'train_split.csv', img_size) | |
| test_ds = CXR14Dataset(data_dir, PROJECT_DIR / 'data' / 'train_split.csv', img_size) | |
| dataloaders = {} | |
| dataloaders['train'] = DataLoader(train_ds, batch_size=batch_size, | |
| shuffle=True, num_workers=num_workers, | |
| pin_memory=True) | |
| dataloaders['val'] = DataLoader(val_ds, batch_size=batch_size, | |
| shuffle=False, num_workers=num_workers, | |
| pin_memory=True) | |
| dataloaders['test'] = DataLoader(test_ds, batch_size=batch_size, | |
| shuffle=False, num_workers=num_workers, | |
| pin_memory=True) | |
| return dataloaders | |
| class CXR14Dataset(Dataset): | |
| def __init__( | |
| self, | |
| data_path: PathLike, | |
| csv_path: PathLike, | |
| img_size: int, | |
| ) -> None: | |
| super().__init__() | |
| assert(os.path.isdir(data_path)) | |
| assert(os.path.isfile(csv_path)) | |
| self.data_path = Path(data_path) | |
| self.df = pd.read_csv(csv_path) | |
| self.img_size = img_size | |
| def __len__(self) -> int: | |
| return len(self.df) | |
| def load_image(self, fname: str) -> Tensor: | |
| img = Image.open(self.data_path /fname).convert('L').resize((self.img_size, self.img_size)) | |
| img = transforms.ToTensor()(img).float() | |
| return img | |
| def __getitem__(self, index) -> Tuple[Tensor, Tensor]: | |
| img = self.load_image(self.df.loc[index, "Image Index"]) | |
| return img | |