80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
import os
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import random
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from .utils import Datum, DatasetBase, listdir_nohidden
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from .oxford_pets import OxfordPets
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template = ['{} texture.']
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class DescribableTextures(DatasetBase):
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dataset_dir = 'dtd'
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def __init__(self, root, num_shots):
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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, 'images')
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self.split_path = os.path.join(self.dataset_dir, 'split_zhou_DescribableTextures.json')
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self.template = template
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train, val, test = OxfordPets.read_split(self.split_path, self.image_dir)
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train = self.generate_fewshot_dataset(train, num_shots=num_shots)
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super().__init__(train_x=train, val=val, test=test)
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@staticmethod
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def read_and_split_data(
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image_dir,
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p_trn=0.5,
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p_val=0.2,
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ignored=[],
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new_cnames=None
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):
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# The data are supposed to be organized into the following structure
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# =============
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# images/
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# dog/
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# cat/
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# horse/
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# =============
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categories = listdir_nohidden(image_dir)
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categories = [c for c in categories if c not in ignored]
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categories.sort()
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p_tst = 1 - p_trn - p_val
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print(f'Splitting into {p_trn:.0%} train, {p_val:.0%} val, and {p_tst:.0%} test')
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def _collate(ims, y, c):
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items = []
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for im in ims:
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item = Datum(
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impath=im,
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label=y, # is already 0-based
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classname=c
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)
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items.append(item)
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return items
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train, val, test = [], [], []
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for label, category in enumerate(categories):
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category_dir = os.path.join(image_dir, category)
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images = listdir_nohidden(category_dir)
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images = [os.path.join(category_dir, im) for im in images]
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random.shuffle(images)
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n_total = len(images)
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n_train = round(n_total * p_trn)
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n_val = round(n_total * p_val)
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n_test = n_total - n_train - n_val
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assert n_train > 0 and n_val > 0 and n_test > 0
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if new_cnames is not None and category in new_cnames:
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category = new_cnames[category]
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train.extend(_collate(images[:n_train], label, category))
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val.extend(_collate(images[n_train:n_train+n_val], label, category))
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test.extend(_collate(images[n_train+n_val:], label, category))
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return train, val, test
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