gluoncv rpn 正负样本

https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/rpn/rpn_target.py

    def forward(self, ious):
        """RPNTargetSampler is only used in data transform with no batch dimension.
        Parameters
        ----------
        ious: (N, M) i.e. (num_anchors, num_gt).
        Returns
        -------
        samples: (num_anchors,) value 1: pos, -1: neg, 0: ignore.
        matches: (num_anchors,) value [0, M).
        """
        matches = mx.nd.argmax(ious, axis=1)

        # samples init with 0 (ignore)
        ious_max_per_anchor = mx.nd.max(ious, axis=1)
        samples = mx.nd.zeros_like(ious_max_per_anchor)

        # set argmax (1, num_gt)
        ious_max_per_gt = mx.nd.max(ious, axis=0, keepdims=True)
        # ious (num_anchor, num_gt) >= argmax (1, num_gt) -> mark row as positive
        mask = mx.nd.broadcast_greater(ious + self._eps, ious_max_per_gt)
        # reduce column (num_anchor, num_gt) -> (num_anchor)
        mask = mx.nd.sum(mask, axis=1)
        # row maybe sampled by 2 columns but still only matches to most overlapping gt
        samples = mx.nd.where(mask, mx.nd.ones_like(samples), samples)

        # set positive overlap to 1
        samples = mx.nd.where(ious_max_per_anchor >= self._pos_iou_thresh,
                              mx.nd.ones_like(samples), samples)
        # set negative overlap to -1
        tmp = (ious_max_per_anchor < self._neg_iou_thresh) * (ious_max_per_anchor >= 0)
        samples = mx.nd.where(tmp, mx.nd.ones_like(samples) * -1, samples)

        # subsample fg labels
        samples = samples.asnumpy()
        num_pos = int((samples > 0).sum())
        if num_pos > self._max_pos:
            disable_indices = np.random.choice(
                np.where(samples > 0)[0], size=(num_pos - self._max_pos), replace=False)
            samples[disable_indices] = 0  # use 0 to ignore

        # subsample bg labels
        num_neg = int((samples < 0).sum())
        # if pos_sample is less than quota, we can have negative samples filling the gap
        max_neg = self._num_sample - min(num_pos, self._max_pos)
        if num_neg > max_neg:
            disable_indices = np.random.choice(
                np.where(samples < 0)[0], size=(num_neg - max_neg), replace=False)
            samples[disable_indices] = 0

        # convert to ndarray
        samples = mx.nd.array(samples, ctx=matches.context)
        return samples, matches