74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
import os
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import pickle
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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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from .dtd import DescribableTextures as DTD
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NEW_CNAMES = {
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"AnnualCrop": "Annual Crop Land",
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"Forest": "Forest",
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"HerbaceousVegetation": "Herbaceous Vegetation Land",
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"Highway": "Highway or Road",
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"Industrial": "Industrial Buildings",
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"Pasture": "Pasture Land",
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"PermanentCrop": "Permanent Crop Land",
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"Residential": "Residential Buildings",
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"River": "River",
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"SeaLake": "Sea or Lake",
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}
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@DATASET_REGISTRY.register()
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class EuroSAT(DatasetBase):
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dataset_dir = "eurosat"
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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, "2750")
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self.split_path = os.path.join(self.dataset_dir, "split_zhou_EuroSAT.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 = DTD.read_and_split_data(self.image_dir, new_cnames=NEW_CNAMES)
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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, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample)
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super().__init__(train_x=train, val=val, test=test)
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def update_classname(self, dataset_old):
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dataset_new = []
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for item_old in dataset_old:
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cname_old = item_old.classname
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cname_new = NEW_CNAMES[cname_old]
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item_new = Datum(impath=item_old.impath, label=item_old.label, classname=cname_new)
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dataset_new.append(item_new)
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return dataset_new
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