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import os
from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
from dassl.utils import listdir_nohidden
from .imagenet import ImageNet
TO_BE_IGNORED = ["README.txt"]
@DATASET_REGISTRY.register()
class ImageNetR(DatasetBase):
"""ImageNet-R(endition).
This dataset is used for testing only.
"""
dataset_dir = "imagenet-rendition"
def __init__(self, cfg):
root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = os.path.join(root, self.dataset_dir)
self.image_dir = os.path.join(self.dataset_dir, "imagenet-r")
text_file = os.path.join(self.dataset_dir, "classnames.txt")
classnames = ImageNet.read_classnames(text_file)
data = self.read_data(classnames)
# if cfg.TRAINER.NAME == "SuPr":
_,self.all_classnames = self.get_lab2cname(data)
super().__init__(train_x=data, test=data)
def read_data(self, classnames):
image_dir = self.image_dir
folders = listdir_nohidden(image_dir, sort=True)
folders = [f for f in folders if f not in TO_BE_IGNORED]
items = []
for label, folder in enumerate(folders):
imnames = listdir_nohidden(os.path.join(image_dir, folder))
classname = classnames[folder]
for imname in imnames:
impath = os.path.join(image_dir, folder, imname)
item = Datum(impath=impath, label=label, classname=classname)
items.append(item)
return items
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