| import os |
| import pickle |
| import random |
| import warnings |
|
|
| from .utils import * |
| from .oxford_pets import OxfordPets |
|
|
| |
| RESISC_CLASSES = [ |
| 'airplane', |
| 'airport', |
| 'baseball diamond', |
| 'basketball court', |
| 'beach', |
| 'bridge', |
| 'chaparral', |
| 'church', |
| 'circular farmland', |
| 'cloud', |
| 'commercial area', |
| 'dense residential', |
| 'desert', |
| 'forest', |
| 'freeway', |
| 'golf course', |
| 'ground track field', |
| 'harbor', |
| 'industrial area', |
| 'intersection', |
| 'island', |
| 'lake', |
| 'meadow', |
| 'medium residential', |
| 'mobile home park', |
| 'mountain', |
| 'overpass', |
| 'palace', |
| 'parking lot', |
| 'railway', |
| 'railway station', |
| 'rectangular farmland', |
| 'river', |
| 'roundabout', |
| 'runway', |
| 'sea ice', |
| 'ship', |
| 'snowberg', |
| 'sparse residential', |
| 'stadium', |
| 'storage tank', |
| 'tennis court', |
| 'terrace', |
| 'thermal power station', |
| 'wetland', |
| ] |
|
|
| |
| RESISC_TEMPLATES = [ |
| 'satellite imagery of {}.', |
| 'aerial imagery of {}.', |
| 'satellite photo of {}.', |
| 'aerial photo of {}.', |
| 'satellite view of {}.', |
| 'aerial view of {}.', |
| 'satellite imagery of a {}.', |
| 'aerial imagery of a {}.', |
| 'satellite photo of a {}.', |
| 'aerial photo of a {}.', |
| 'satellite view of a {}.', |
| 'aerial view of a {}.', |
| 'satellite imagery of the {}.', |
| 'aerial imagery of the {}.', |
| 'satellite photo of the {}.', |
| 'aerial photo of the {}.', |
| 'satellite view of the {}.', |
| 'aerial view of the {}.', |
| ] |
|
|
| RESISC_DEBUG = os.environ.get("RESISC_DEBUG", "0") not in ("0", "false", "False", "") |
|
|
| def _dbg(msg: str): |
| if RESISC_DEBUG: |
| print(f"[RESISC45][DEBUG] {msg}") |
|
|
|
|
| def _norm_name(s: str) -> str: |
| s = s.lower().strip() |
| for ch in [" ", "_", "-", "."]: |
| s = s.replace(ch, "") |
| return s |
|
|
|
|
| class RESISC45(DatasetBase): |
|
|
| dataset_dir = "resisc45" |
|
|
| def __init__(self, root, num_shots=0, seed=1, subsample_classes='all'): |
| root = os.path.abspath(os.path.expanduser(root)) |
| self.dataset_dir = os.path.join(root, self.dataset_dir) |
| self.image_dir = self.dataset_dir |
| self.split_path = os.path.join(self.dataset_dir, "split_custom_RESISC45.json") |
| self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") |
| mkdir_if_missing(self.split_fewshot_dir) |
| _dbg(f"dataset_dir={self.dataset_dir}") |
|
|
| if os.path.exists(self.split_path): |
| try: |
| train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) |
| except Exception as e: |
| warnings.warn(f"RESISC45: failed to read split file '{self.split_path}'; rebuilding. Error: {e}") |
| train, val, test = self.read_and_split_data(self.image_dir) |
| try: |
| OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) |
| except Exception as e2: |
| warnings.warn(f"RESISC45: failed to save rebuilt split to '{self.split_path}': {e2}") |
| else: |
| train, val, test = self.read_and_split_data(self.image_dir) |
| try: |
| OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) |
| except Exception as e: |
| warnings.warn(f"RESISC45: failed to save split to '{self.split_path}': {e}") |
|
|
| if num_shots >= 1: |
| 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) |
|
|
| train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample_classes) |
| self.templates = RESISC_TEMPLATES |
| super().__init__(train_x=train, val=val, test=test) |
|
|
| @staticmethod |
| def read_and_split_data(image_dir, p_trn=0.5, p_val=0.2): |
| try: |
| categories = listdir_nohidden(image_dir, sort=True) |
| except Exception as e: |
| warnings.warn(f"RESISC45: failed to list directory '{image_dir}': {e}") |
| raise |
| categories = [c for c in categories if os.path.isdir(os.path.join(image_dir, c))] |
| cat_norm = {_norm_name(c): c for c in categories} |
|
|
| missing = [] |
| class_to_dir = {} |
| for cname in RESISC_CLASSES: |
| key = _norm_name(cname) |
| if key in cat_norm: |
| class_to_dir[cname] = cat_norm[key] |
| else: |
| missing.append(cname) |
| if missing: |
| warnings.warn(f"RESISC45: missing class folders for {len(missing)} classes: {missing[:5]}{' ...' if len(missing)>5 else ''}") |
|
|
| def _collate(paths, y, cname): |
| return [Datum(impath=p, label=y, classname=cname) for p in paths] |
|
|
| train, val, test = [], [], [] |
| for y, cname in enumerate(RESISC_CLASSES): |
| if cname not in class_to_dir: |
| continue |
| cdir = os.path.join(image_dir, class_to_dir[cname]) |
| try: |
| images = listdir_nohidden(cdir, sort=False) |
| except Exception as e: |
| warnings.warn(f"RESISC45: failed to list class folder '{cdir}': {e}") |
| images = [] |
| images = [os.path.join(cdir, im) for im in images] |
| random.shuffle(images) |
| n_total = len(images) |
| if n_total == 0: |
| warnings.warn(f"RESISC45: empty class folder {cdir}; skipping") |
| continue |
| n_train = round(n_total * p_trn) |
| n_val = round(n_total * p_val) |
| n_test = n_total - n_train - n_val |
| if not (n_train > 0 and n_val > 0 and n_test > 0): |
| |
| if n_total >= 3: |
| n_train, n_val, n_test = 1, 1, n_total - 2 |
| elif n_total == 2: |
| n_train, n_val, n_test = 1, 1, 0 |
| else: |
| n_train, n_val, n_test = 1, 0, 0 |
|
|
| train.extend(_collate(images[:n_train], y, cname)) |
| val.extend(_collate(images[n_train:n_train + n_val], y, cname)) |
| test.extend(_collate(images[n_train + n_val:], y, cname)) |
|
|
| return train, val, test |
|
|
