def _retina_detach(self, out):
''' ##### Author [email protected]
Solving bounding boxes.
Parameters
----------
out: map object of staggered scores and deltas.
scores, deltas = next(out), next(out)
Each scores has shape [N, A*4, H, W].
Each deltas has shape [N, A*4, H, W].
N is the batch size.
A is the shape[0] of base anchors declared in the fpn dict.
H, W is the heights and widths of the anchors grid,
based on the stride and input image's height and width.
Returns
-------
Generator of list, each list has [boxes, scores].
Usage
-----
>>> np.block(list(self._retina_solving(out)))
'''
buffer, anchors = out[0].asnumpy(), out[1]
mask = buffer[:, 4] > self.threshold
deltas = buffer[mask]
nonlinear_pred(anchors[mask], deltas)
deltas[:, :4] /= self.scale
return deltas
def deal_with_fpn(fpn, scores, deltas):
anchors = self._anchors_plane(*deltas.shape[-2:], *fpn).reshape(
(-1, 4))
scores = scores[:, fpn[1].shape[0]:, :, :].transpose(
(0, 2, 3, 1)).reshape((-1, 1))
deltas = deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
mask = scores.reshape((-1, )) > self.threshold
proposals = deltas[mask]
nonlinear_pred(anchors[mask], proposals)
return [proposals / scale, scores[mask]]
def _retina_solving(self, out):
''' ##### Author [email protected]
Solving bounding boxes.
Parameters
----------
out: map object of staggered scores and deltas.
scores, deltas = next(out), next(out)
Each scores has shape [N, A*4, H, W].
Each deltas has shape [N, A*4, H, W].
N is the batch size.
A is the shape[0] of base anchors declared in the fpn dict.
H, W is the heights and widths of the anchors grid,
based on the stride and input image's height and width.
Returns
-------
Generator of list, each list has [boxes, scores].
Usage
-----
>>> np.block(list(self._retina_solving(out)))
'''
for fpn in self._fpn_anchors:
scores, deltas = next(out)[:, fpn[1].shape[0]:, ...], next(out)
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
mask = scores.ravel() > self.threshold
anchors = self._get_runtime_anchors(*deltas.shape[-2:], *fpn)[mask]
deltas = deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))[mask]
nonlinear_pred(anchors, deltas)
yield [deltas / self.scale, scores[mask]]