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