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https://github.com/SillyTavern/SillyTavern-Extras.git
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73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import logging
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import numpy as np
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from . import BaseWrapperDataset
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logger = logging.getLogger(__name__)
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class SubsampleDataset(BaseWrapperDataset):
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"""Subsamples a given dataset by a specified ratio. Subsampling is done on the number of examples
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Args:
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dataset (~torch.utils.data.Dataset): dataset to subsample
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size_ratio(float): the ratio to subsample to. must be between 0 and 1 (exclusive)
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"""
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def __init__(self, dataset, size_ratio, shuffle=False):
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super().__init__(dataset)
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assert size_ratio < 1
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self.actual_size = np.ceil(len(dataset) * size_ratio).astype(int)
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self.indices = np.random.choice(
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list(range(len(self.dataset))), self.actual_size, replace=False
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)
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self.shuffle = shuffle
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logger.info(
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"subsampled dataset from {} to {} (ratio={})".format(
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len(self.dataset), self.actual_size, size_ratio
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)
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)
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def __getitem__(self, index):
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return self.dataset[self.indices[index]]
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def __len__(self):
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return self.actual_size
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def collater(self, samples):
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return self.dataset.collater(samples)
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@property
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def sizes(self):
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return self.dataset.sizes[self.indices]
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@property
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def name(self):
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return self.dataset.name
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def num_tokens(self, index):
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return self.dataset.num_tokens(self.indices[index])
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def size(self, index):
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return self.dataset.size(self.indices[index])
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def ordered_indices(self):
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"""Return an ordered list of indices. Batches will be constructed based
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on this order."""
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if self.shuffle:
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order = [np.random.permutation(len(self))]
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else:
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order = [np.arange(len(self))]
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order.append(self.sizes)
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return np.lexsort(order)
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def prefetch(self, indices):
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self.dataset.prefetch(self.indices[indices])
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