def test_filter_scores_and_topk():
score = torch.tensor([[0.1, 0.3, 0.2], [0.12, 0.7, 0.9], [0.02, 0.8, 0.08],
[0.4, 0.1, 0.08]])
bbox_pred = torch.tensor([[0.2, 0.3], [0.4, 0.7], [0.1, 0.1], [0.5, 0.1]])
score_thr = 0.15
nms_pre = 4
# test results type error
with pytest.raises(NotImplementedError):
filter_scores_and_topk(score, score_thr, nms_pre, (score, ))
filtered_results = filter_scores_and_topk(
score, score_thr, nms_pre, results=dict(bbox_pred=bbox_pred))
filtered_score, labels, keep_idxs, results = filtered_results
assert filtered_score.allclose(torch.tensor([0.9, 0.8, 0.7, 0.4]))
assert labels.allclose(torch.tensor([2, 1, 1, 0]))
assert keep_idxs.allclose(torch.tensor([1, 2, 1, 3]))
assert results['bbox_pred'].allclose(
torch.tensor([[0.4, 0.7], [0.1, 0.1], [0.4, 0.7], [0.5, 0.1]]))
def _get_bboxes_single(self,
cls_score_list,
bbox_pred_list,
score_factor_list,
mlvl_priors,
img_meta,
cfg,
rescale=False,
with_nms=True,
**kwargs):
"""Transform outputs of a single image into bbox predictions.
Args:
cls_score_list (list[Tensor]): Box scores from all scale
levels of a single image, each item has shape
(num_priors * num_classes, H, W).
bbox_pred_list (list[Tensor]): Box energies / deltas from
all scale levels of a single image, each item has shape
(num_priors * 4, H, W).
score_factor_list (list[Tensor]): Score factor from all scale
levels of a single image. Fovea head does not need this value.
mlvl_priors (list[Tensor]): Each element in the list is
the priors of a single level in feature pyramid, has shape
(num_priors, 2).
img_meta (dict): Image meta info.
cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
Returns:
tuple[Tensor]: Results of detected bboxes and labels. If with_nms
is False and mlvl_score_factor is None, return mlvl_bboxes and
mlvl_scores, else return mlvl_bboxes, mlvl_scores and
mlvl_score_factor. Usually with_nms is False is used for aug
test. If with_nms is True, then return the following format
- det_bboxes (Tensor): Predicted bboxes with shape \
[num_bboxes, 5], where the first 4 columns are bounding \
box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
column are scores between 0 and 1.
- det_labels (Tensor): Predicted labels of the corresponding \
box with shape [num_bboxes].
"""
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_score_list) == len(bbox_pred_list)
img_shape = img_meta['img_shape']
nms_pre = cfg.get('nms_pre', -1)
mlvl_bboxes = []
mlvl_scores = []
mlvl_labels = []
for level_idx, (cls_score, bbox_pred, stride, base_len, priors) in \
enumerate(zip(cls_score_list, bbox_pred_list, self.strides,
self.base_edge_list, mlvl_priors)):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
# After https://github.com/open-mmlab/mmdetection/pull/6268/,
# this operation keeps fewer bboxes under the same `nms_pre`.
# There is no difference in performance for most models. If you
# find a slight drop in performance, you can set a larger
# `nms_pre` than before.
results = filter_scores_and_topk(
scores, cfg.score_thr, nms_pre,
dict(bbox_pred=bbox_pred, priors=priors))
scores, labels, _, filtered_results = results
bbox_pred = filtered_results['bbox_pred']
priors = filtered_results['priors']
bboxes = self._bbox_decode(priors, bbox_pred, base_len, img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_labels.append(labels)
return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
img_meta['scale_factor'], cfg, rescale,
with_nms)
def _get_bboxes_single(self,
cls_score_list,
bbox_pred_list,
score_factor_list,
mlvl_priors,
img_meta,
cfg,
rescale=False,
with_nms=True,
**kwargs):
"""Transform outputs of a single image into bbox predictions.
Args:
cls_score_list (list[Tensor]): Box scores from all scale
levels of a single image, each item has shape
(num_priors * num_classes, H, W).
bbox_pred_list (list[Tensor]): Box energies / deltas from
all scale levels of a single image, each item has shape
(num_priors * 4, H, W).
score_factor_list (list[Tensor]): Score factor from all scale
levels of a single image, each item has shape
(num_priors * 1, H, W).
mlvl_priors (list[Tensor]): Each element in the list is
the priors of a single level in feature pyramid. In all
anchor-based methods, it has shape (num_priors, 4). In
all anchor-free methods, it has shape (num_priors, 2)
when `with_stride=True`, otherwise it still has shape
(num_priors, 4).
img_meta (dict): Image meta info.
cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
Returns:
tuple[Tensor]: Results of detected bboxes and labels. If with_nms
is False and mlvl_score_factor is None, return mlvl_bboxes and
mlvl_scores, else return mlvl_bboxes, mlvl_scores and
mlvl_score_factor. Usually with_nms is False is used for aug
test. If with_nms is True, then return the following format
- det_bboxes (Tensor): Predicted bboxes with shape \
[num_bboxes, 5], where the first 4 columns are bounding \
box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
column are scores between 0 and 1.
- det_labels (Tensor): Predicted labels of the corresponding \
box with shape [num_bboxes].
"""
if score_factor_list[0] is None:
# e.g. Retina, FreeAnchor, etc.
with_score_factors = False
else:
# e.g. FCOS, PAA, ATSS, etc.
with_score_factors = True
cfg = self.test_cfg if cfg is None else cfg
img_shape = img_meta['img_shape']
nms_pre = cfg.get('nms_pre', -1)
mlvl_bboxes = []
mlvl_scores = []
mlvl_labels = []
if with_score_factors:
mlvl_score_factors = []
else:
mlvl_score_factors = None
for level_idx, (cls_score, bbox_pred, score_factor, priors) in \
enumerate(zip(cls_score_list, bbox_pred_list,
score_factor_list, mlvl_priors)):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
if with_score_factors:
score_factor = score_factor.permute(1, 2,
0).reshape(-1).sigmoid()
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
# remind that we set FG labels to [0, num_class-1]
# since mmdet v2.0
# BG cat_id: num_class
scores = cls_score.softmax(-1)[:, :-1]
# After https://github.com/open-mmlab/mmdetection/pull/6268/,
# this operation keeps fewer bboxes under the same `nms_pre`.
# There is no difference in performance for most models. If you
# find a slight drop in performance, you can set a larger
# `nms_pre` than before.
results = filter_scores_and_topk(
scores, cfg.score_thr, nms_pre,
dict(bbox_pred=bbox_pred, priors=priors))
scores, labels, keep_idxs, filtered_results = results
bbox_pred = filtered_results['bbox_pred']
priors = filtered_results['priors']
if with_score_factors:
score_factor = score_factor[keep_idxs]
bboxes = self.bbox_coder.decode(priors,
bbox_pred,
max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_labels.append(labels)
if with_score_factors:
mlvl_score_factors.append(score_factor)
return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
img_meta['scale_factor'], cfg, rescale,
with_nms, mlvl_score_factors, **kwargs)
def _get_bboxes_single(self,
cls_scores,
bbox_cls_preds,
bbox_reg_preds,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False):
cfg = self.test_cfg if cfg is None else cfg
nms_pre = cfg.get('nms_pre', -1)
mlvl_bboxes = []
mlvl_scores = []
mlvl_confids = []
mlvl_labels = []
assert len(cls_scores) == len(bbox_cls_preds) == len(
bbox_reg_preds) == len(mlvl_anchors)
for cls_score, bbox_cls_pred, bbox_reg_pred, anchors in zip(
cls_scores, bbox_cls_preds, bbox_reg_preds, mlvl_anchors):
assert cls_score.size()[-2:] == bbox_cls_pred.size(
)[-2:] == bbox_reg_pred.size()[-2::]
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
scores = cls_score.softmax(-1)[:, :-1]
bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape(
-1, self.side_num * 4)
bbox_reg_pred = bbox_reg_pred.permute(1, 2, 0).reshape(
-1, self.side_num * 4)
# After https://github.com/open-mmlab/mmdetection/pull/6268/,
# this operation keeps fewer bboxes under the same `nms_pre`.
# There is no difference in performance for most models. If you
# find a slight drop in performance, you can set a larger
# `nms_pre` than before.
results = filter_scores_and_topk(
scores, cfg.score_thr, nms_pre,
dict(
anchors=anchors,
bbox_cls_pred=bbox_cls_pred,
bbox_reg_pred=bbox_reg_pred))
scores, labels, _, filtered_results = results
anchors = filtered_results['anchors']
bbox_cls_pred = filtered_results['bbox_cls_pred']
bbox_reg_pred = filtered_results['bbox_reg_pred']
bbox_preds = [
bbox_cls_pred.contiguous(),
bbox_reg_pred.contiguous()
]
bboxes, confids = self.bbox_coder.decode(
anchors.contiguous(), bbox_preds, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_confids.append(confids)
mlvl_labels.append(labels)
return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
scale_factor, cfg, rescale, True,
mlvl_confids)
