47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
import os
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from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
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from dassl.utils import listdir_nohidden
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from .imagenet import ImageNet
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TO_BE_IGNORED = ["README.txt"]
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@DATASET_REGISTRY.register()
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class ImageNetR(DatasetBase):
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"""ImageNet-R(endition).
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This dataset is used for testing only.
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"""
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dataset_dir = "imagenet-rendition"
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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, "imagenet-r")
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text_file = os.path.join(self.dataset_dir, "classnames.txt")
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classnames = ImageNet.read_classnames(text_file)
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data = self.read_data(classnames)
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super().__init__(train_x=data, test=data)
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def read_data(self, classnames):
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image_dir = self.image_dir
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folders = listdir_nohidden(image_dir, sort=True)
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folders = [f for f in folders if f not in TO_BE_IGNORED]
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items = []
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for label, folder in enumerate(folders):
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imnames = listdir_nohidden(os.path.join(image_dir, folder))
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classname = classnames[folder]
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for imname in imnames:
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impath = os.path.join(image_dir, folder, imname)
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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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