| import os | |
| from PIL import Image | |
| from torchvision.datasets import VisionDataset | |
| from torchvision.datasets.folder import find_classes, make_dataset | |
| from dataset_lib.config import Constants as c | |
| def get_dataset(dataset, transform=None, workdir=c.WORKDIR): | |
| data_dir = os.path.join(workdir, "data") | |
| if dataset == "imagenette": | |
| root_dir = os.path.join(data_dir, "imagenette2") | |
| train_dataset = Imagenette(root_dir, train=True, transform=transform) | |
| test_dataset = Imagenette(root_dir, train=False, transform=transform) | |
| return {"train": train_dataset, "test": test_dataset} | |
| class Imagenette(VisionDataset): | |
| _WNID_TO_CLASS = { | |
| "n01440764": ("tench", "Tinca tinca"), | |
| "n02102040": ("English springer", "English springer spaniel"), | |
| "n02979186": ("cassette player",), | |
| "n03000684": ("chainsaw", "chain saw"), | |
| "n03028079": ("church", "church building"), | |
| "n03394916": ("French horn", "horn"), | |
| "n03417042": ("garbage truck", "dustcart"), | |
| "n03425413": ("gas pump", "gasoline pump", "petrol pump", "island dispenser"), | |
| "n03445777": ("golf ball",), | |
| "n03888257": ("parachute", "chute"), | |
| } | |
| def __init__(self, root, train=True, transform=None): | |
| super().__init__(root, transform=transform) | |
| self.train = train | |
| self._split = "train" if train else "test" | |
| self._image_root = os.path.join(root, self._split) | |
| self.wnids, self.wnid_to_idx = find_classes(self._image_root) | |
| self.classes = [self._WNID_TO_CLASS[wnid][0] for wnid in self.wnids] | |
| self._samples = make_dataset( | |
| self._image_root, self.wnid_to_idx, extensions=".jpeg" | |
| ) | |
| def __len__(self): | |
| return len(self._samples) | |
| def __getitem__(self, idx): | |
| path, label = self._samples[idx] | |
| image = Image.open(path).convert("RGB") | |
| if self.transform is not None: | |
| image = self.transform(image) | |
| return image, label | |