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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch


class BalancedPositiveNegativeSampler(object):
    """

    This class samples batches, ensuring that they contain a fixed proportion of positives

    """

    def __init__(self, batch_size_per_image, positive_fraction):
        """

        Arguments:

            batch_size_per_image (int): number of elements to be selected per image

            positive_fraction (float): percentace of positive elements per batch

        """
        self.batch_size_per_image = batch_size_per_image
        self.positive_fraction = positive_fraction

    def __call__(self, matched_idxs):
        """

        Arguments:

            matched idxs: list of tensors containing -1, 0 or positive values.

                Each tensor corresponds to a specific image.

                -1 values are ignored, 0 are considered as negatives and > 0 as

                positives.



        Returns:

            pos_idx (list[tensor])

            neg_idx (list[tensor])



        Returns two lists of binary masks for each image.

        The first list contains the positive elements that were selected,

        and the second list the negative example.

        """
        pos_idx = []
        neg_idx = []
        for matched_idxs_per_image in matched_idxs:
            positive = torch.nonzero(matched_idxs_per_image >= 1).squeeze(1)
            negative = torch.nonzero(matched_idxs_per_image == 0).squeeze(1)

            num_pos = int(self.batch_size_per_image * self.positive_fraction)
            # protect against not enough positive examples
            num_pos = min(positive.numel(), num_pos)
            num_neg = self.batch_size_per_image - num_pos
            # protect against not enough negative examples
            num_neg = min(negative.numel(), num_neg)

            # randomly select positive and negative examples
            perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
            perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

            pos_idx_per_image = positive[perm1]
            neg_idx_per_image = negative[perm2]

            # create binary mask from indices
            pos_idx_per_image_mask = torch.zeros_like(
                matched_idxs_per_image, dtype=torch.bool
            )
            neg_idx_per_image_mask = torch.zeros_like(
                matched_idxs_per_image, dtype=torch.bool
            )
            pos_idx_per_image_mask[pos_idx_per_image] = 1
            neg_idx_per_image_mask[neg_idx_per_image] = 1

            pos_idx.append(pos_idx_per_image_mask)
            neg_idx.append(neg_idx_per_image_mask)

        return pos_idx, neg_idx