81 lines
3.3 KiB
Python
81 lines
3.3 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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@DATASET_REGISTRY.register()
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class SUN397(DatasetBase):
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dataset_dir = "sun397"
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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, "SUN397")
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self.split_path = os.path.join(self.dataset_dir, "split_zhou_SUN397.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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classnames = []
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with open(os.path.join(self.dataset_dir, "ClassName.txt"), "r") as f:
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lines = f.readlines()
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for line in lines:
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line = line.strip()[1:] # remove /
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classnames.append(line)
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cname2lab = {c: i for i, c in enumerate(classnames)}
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trainval = self.read_data(cname2lab, "Training_01.txt")
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test = self.read_data(cname2lab, "Testing_01.txt")
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train, val = OxfordPets.split_trainval(trainval)
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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 read_data(self, cname2lab, text_file):
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text_file = os.path.join(self.dataset_dir, text_file)
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items = []
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with open(text_file, "r") as f:
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lines = f.readlines()
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for line in lines:
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imname = line.strip()[1:] # remove /
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classname = os.path.dirname(imname)
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label = cname2lab[classname]
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impath = os.path.join(self.image_dir, imname)
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names = classname.split("/")[1:] # remove 1st letter
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names = names[::-1] # put words like indoor/outdoor at first
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classname = " ".join(names)
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item = Datum(impath=impath, label=label, classname=classname)
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items.append(item)
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return items
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