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| import torch
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|
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| from annotator.oneformer.detectron2.layers import nonzero_tuple
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|
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| __all__ = ["subsample_labels"]
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| def subsample_labels(
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| labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int
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| ):
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| """
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| Return `num_samples` (or fewer, if not enough found)
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| random samples from `labels` which is a mixture of positives & negatives.
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| It will try to return as many positives as possible without
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| exceeding `positive_fraction * num_samples`, and then try to
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| fill the remaining slots with negatives.
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| Args:
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| labels (Tensor): (N, ) label vector with values:
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| * -1: ignore
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| * bg_label: background ("negative") class
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| * otherwise: one or more foreground ("positive") classes
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| num_samples (int): The total number of labels with value >= 0 to return.
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| Values that are not sampled will be filled with -1 (ignore).
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| positive_fraction (float): The number of subsampled labels with values > 0
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| is `min(num_positives, int(positive_fraction * num_samples))`. The number
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| of negatives sampled is `min(num_negatives, num_samples - num_positives_sampled)`.
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| In order words, if there are not enough positives, the sample is filled with
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| negatives. If there are also not enough negatives, then as many elements are
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| sampled as is possible.
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| bg_label (int): label index of background ("negative") class.
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|
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| Returns:
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| pos_idx, neg_idx (Tensor):
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| 1D vector of indices. The total length of both is `num_samples` or fewer.
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| """
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| positive = nonzero_tuple((labels != -1) & (labels != bg_label))[0]
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| negative = nonzero_tuple(labels == bg_label)[0]
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|
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| num_pos = int(num_samples * positive_fraction)
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|
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| num_pos = min(positive.numel(), num_pos)
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| num_neg = num_samples - num_pos
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|
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| num_neg = min(negative.numel(), num_neg)
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| perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
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| perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]
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| pos_idx = positive[perm1]
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| neg_idx = negative[perm2]
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| return pos_idx, neg_idx
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|
