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