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import os |
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import pickle |
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from scipy.io import loadmat |
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from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase |
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from dassl.utils import mkdir_if_missing |
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from .oxford_pets import OxfordPets |
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@DATASET_REGISTRY.register() |
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class StanfordCars(DatasetBase): |
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dataset_dir = "stanford_cars" |
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def __init__(self, cfg): |
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root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) |
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self.dataset_dir = os.path.join(root, self.dataset_dir) |
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self.split_path = os.path.join(self.dataset_dir, "split_zhou_StanfordCars.json") |
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self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") |
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mkdir_if_missing(self.split_fewshot_dir) |
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if os.path.exists(self.split_path): |
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train, val, test = OxfordPets.read_split(self.split_path, self.dataset_dir) |
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else: |
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trainval_file = os.path.join(self.dataset_dir, "devkit", "cars_train_annos.mat") |
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test_file = os.path.join(self.dataset_dir, "cars_test_annos_withlabels.mat") |
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meta_file = os.path.join(self.dataset_dir, "devkit", "cars_meta.mat") |
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trainval = self.read_data("cars_train", trainval_file, meta_file) |
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test = self.read_data("cars_test", test_file, meta_file) |
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train, val = OxfordPets.split_trainval(trainval) |
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OxfordPets.save_split(train, val, test, self.split_path, self.dataset_dir) |
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num_shots = cfg.DATASET.NUM_SHOTS |
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if num_shots >= 1: |
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seed = cfg.SEED |
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preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") |
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if os.path.exists(preprocessed): |
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print(f"Loading preprocessed few-shot data from {preprocessed}") |
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with open(preprocessed, "rb") as file: |
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data = pickle.load(file) |
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train, val = data["train"], data["val"] |
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else: |
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train = self.generate_fewshot_dataset(train, num_shots=num_shots) |
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val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) |
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data = {"train": train, "val": val} |
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print(f"Saving preprocessed few-shot data to {preprocessed}") |
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with open(preprocessed, "wb") as file: |
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pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) |
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subsample = cfg.DATASET.SUBSAMPLE_CLASSES |
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train, _, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) |
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super().__init__(train_x=train, val=test, test=test) |
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self.all_classnames = OxfordPets.get_all_classnames(train, val, test) |
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def read_data(self, image_dir, anno_file, meta_file): |
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anno_file = loadmat(anno_file)["annotations"][0] |
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meta_file = loadmat(meta_file)["class_names"][0] |
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items = [] |
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for i in range(len(anno_file)): |
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imname = anno_file[i]["fname"][0] |
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impath = os.path.join(self.dataset_dir, image_dir, imname) |
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label = anno_file[i]["class"][0, 0] |
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label = int(label) - 1 |
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classname = meta_file[label][0] |
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names = classname.split(" ") |
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year = names.pop(-1) |
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names.insert(0, year) |
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classname = " ".join(names) |
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item = Datum(impath=impath, label=label, classname=classname) |
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items.append(item) |
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return items |
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