datasets/sun397.py [35:67]:
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            train, val = OxfordPets.split_trainval(trainval)
            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, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample)

        super().__init__(train_x=train, val=val, test=test)

    def read_data(self, cname2lab, text_file):
        text_file = os.path.join(self.dataset_dir, text_file)
        items = []

        with open(text_file, "r") as f:
            lines = f.readlines()
            for line in lines:
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datasets/ucf101.py [38:70]:
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            train, val = OxfordPets.split_trainval(trainval)
            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, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample)

        super().__init__(train_x=train, val=val, test=test)

    def read_data(self, cname2lab, text_file):
        text_file = os.path.join(self.dataset_dir, text_file)
        items = []

        with open(text_file, "r") as f:
            lines = f.readlines()
            for line in lines:
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