| | import os |
| | import pickle |
| |
|
| | from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase |
| | from dassl.utils import mkdir_if_missing |
| |
|
| | from .oxford_pets import OxfordPets |
| | from .dtd import DescribableTextures as DTD |
| |
|
| |
|
| | @DATASET_REGISTRY.register() |
| | class Food101(DatasetBase): |
| |
|
| | dataset_dir = "food-101" |
| |
|
| | def __init__(self, cfg): |
| | root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) |
| | self.dataset_dir = os.path.join(root, self.dataset_dir) |
| | self.image_dir = os.path.join(self.dataset_dir, "images") |
| | self.split_path = os.path.join(self.dataset_dir, "split_zhou_Food101.json") |
| | self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") |
| | mkdir_if_missing(self.split_fewshot_dir) |
| |
|
| | if os.path.exists(self.split_path): |
| | train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) |
| | else: |
| | train, val, test = DTD.read_and_split_data(self.image_dir) |
| | OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) |
| |
|
| | num_shots = cfg.DATASET.NUM_SHOTS |
| | if num_shots >= 1: |
| | seed = cfg.SEED |
| | preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") |
| | |
| | if os.path.exists(preprocessed): |
| | print(f"Loading preprocessed few-shot data from {preprocessed}") |
| | with open(preprocessed, "rb") as file: |
| | data = pickle.load(file) |
| | train, val = data["train"], data["val"] |
| | else: |
| | train = self.generate_fewshot_dataset(train, num_shots=num_shots) |
| | val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) |
| | data = {"train": train, "val": val} |
| | print(f"Saving preprocessed few-shot data to {preprocessed}") |
| | with open(preprocessed, "wb") as file: |
| | pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) |
| |
|
| | subsample = cfg.DATASET.SUBSAMPLE_CLASSES |
| | |
| | train, _, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) |
| | super().__init__(train_x=train, val=test, test=test) |
| |
|
| | |
| | self.all_classnames = OxfordPets.get_all_classnames(train, val, test) |
