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import os |
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import pickle |
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import random |
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from scipy.io import loadmat |
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from collections import defaultdict |
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from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase |
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from dassl.utils import read_json, mkdir_if_missing |
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from .oxford_pets import OxfordPets |
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@DATASET_REGISTRY.register() |
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class OxfordFlowers(DatasetBase): |
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dataset_dir = "oxford_flowers" |
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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.image_dir = os.path.join(self.dataset_dir, "jpg") |
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self.label_file = os.path.join(self.dataset_dir, "imagelabels.mat") |
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self.lab2cname_file = os.path.join(self.dataset_dir, "cat_to_name.json") |
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self.split_path = os.path.join(self.dataset_dir, "split_zhou_OxfordFlowers.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.image_dir) |
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else: |
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train, val, test = self.read_data() |
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OxfordPets.save_split(train, val, test, self.split_path, self.image_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): |
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tracker = defaultdict(list) |
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label_file = loadmat(self.label_file)["labels"][0] |
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for i, label in enumerate(label_file): |
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imname = f"image_{str(i + 1).zfill(5)}.jpg" |
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impath = os.path.join(self.image_dir, imname) |
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label = int(label) |
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tracker[label].append(impath) |
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print("Splitting data into 50% train, 20% val, and 30% test") |
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def _collate(ims, y, c): |
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items = [] |
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for im in ims: |
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item = Datum(impath=im, label=y - 1, classname=c) |
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items.append(item) |
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return items |
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lab2cname = read_json(self.lab2cname_file) |
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train, val, test = [], [], [] |
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for label, impaths in tracker.items(): |
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random.shuffle(impaths) |
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n_total = len(impaths) |
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n_train = round(n_total * 0.5) |
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n_val = round(n_total * 0.2) |
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n_test = n_total - n_train - n_val |
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assert n_train > 0 and n_val > 0 and n_test > 0 |
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cname = lab2cname[str(label)] |
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train.extend(_collate(impaths[:n_train], label, cname)) |
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val.extend(_collate(impaths[n_train : n_train + n_val], label, cname)) |
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test.extend(_collate(impaths[n_train + n_val :], label, cname)) |
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return train, val, test |
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