67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
import os
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import random
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from scipy.io import loadmat
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from collections import defaultdict
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from .oxford_pets import OxfordPets
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from .utils import Datum, DatasetBase, read_json
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template = ['a photo of a {}, a type of flower.']
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class OxfordFlowers(DatasetBase):
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dataset_dir = 'oxford_flowers'
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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, 'jpg')
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self.label_file = os.path.join(self.dataset_dir, 'imagelabels.mat')
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self.lab2cname_file = os.path.join(self.dataset_dir, 'cat_to_name.json')
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self.split_path = os.path.join(self.dataset_dir, 'split_zhou_OxfordFlowers.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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def read_data(self):
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tracker = defaultdict(list)
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label_file = loadmat(self.label_file)['labels'][0]
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for i, label in enumerate(label_file):
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imname = f'image_{str(i + 1).zfill(5)}.jpg'
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impath = os.path.join(self.image_dir, imname)
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label = int(label)
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tracker[label].append(impath)
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print('Splitting data into 50% train, 20% val, and 30% 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-1, # convert to 0-based label
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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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lab2cname = read_json(self.lab2cname_file)
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train, val, test = [], [], []
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for label, impaths in tracker.items():
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random.shuffle(impaths)
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n_total = len(impaths)
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n_train = round(n_total * 0.5)
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n_val = round(n_total * 0.2)
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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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cname = lab2cname[str(label)]
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train.extend(_collate(impaths[:n_train], label, cname))
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val.extend(_collate(impaths[n_train:n_train+n_val], label, cname))
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test.extend(_collate(impaths[n_train+n_val:], label, cname))
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return train, val, test |