30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
import torch, copy, random
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import torch.utils.data as data
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class SearchDataset(data.Dataset):
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def __init__(self, name, data, train_split, valid_split, check=True):
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self.datasetname = name
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self.data = data
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self.train_split = train_split.copy()
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self.valid_split = valid_split.copy()
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if check:
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intersection = set(train_split).intersection(set(valid_split))
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assert len(intersection) == 0, 'the splitted train and validation sets should have no intersection'
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self.length = len(self.train_split)
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def __repr__(self):
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return ('{name}(name={datasetname}, train={tr_L}, valid={val_L})'.format(name=self.__class__.__name__, datasetname=self.datasetname, tr_L=len(self.train_split), val_L=len(self.valid_split)))
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def __len__(self):
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return self.length
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def __getitem__(self, index):
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assert index >= 0 and index < self.length, 'invalid index = {:}'.format(index)
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train_index = self.train_split[index]
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valid_index = random.choice( self.valid_split )
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train_image, train_label = self.data[train_index]
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valid_image, valid_label = self.data[valid_index]
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return train_image, train_label, valid_image, valid_label
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