64 lines
2.4 KiB
Python
64 lines
2.4 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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import deepcore.methods as s_method
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import numpy as np
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IGNORED = ["BACKGROUND_Google", "Faces_easy"]
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NEW_CNAMES = {
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"airplanes": "airplane",
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"Faces": "face",
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"Leopards": "leopard",
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"Motorbikes": "motorbike",
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}
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@DATASET_REGISTRY.register()
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class Caltech101(DatasetBase):
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dataset_dir = "caltech-101"
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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, "101_ObjectCategories")
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self.split_path = os.path.join(self.dataset_dir, "split_zhou_Caltech101.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, ignored=IGNORED, 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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