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