def simple_test(self, img, img_meta, proposals=None, rescale=False):
x = self.extract_feat(img)
proposal_list = self.simple_test_rpn(
x, img_meta, self.test_cfg.rpn) if proposals is None else proposals
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
rcnn_test_cfg = self.test_cfg.rcnn
rois = bbox2roi(proposal_list)
bbox_feats = self.bbox_roi_extractor(
x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = self.bbox_head(bbox_feats)
bbox_label = cls_score.argmax(dim=1)
rrois = self.bbox_head.regress_by_class_rbbox(roi2droi(rois),
bbox_label, bbox_pred,
img_meta[0])
rrois_enlarge = copy.deepcopy(rrois)
rrois_enlarge[:,
3] = rrois_enlarge[:,
3] * self.rbbox_roi_extractor.w_enlarge
rrois_enlarge[:,
4] = rrois_enlarge[:,
4] * self.rbbox_roi_extractor.h_enlarge
rbbox_feats = self.rbbox_roi_extractor(
x[:len(self.rbbox_roi_extractor.featmap_strides)], rrois_enlarge)
if self.with_shared_head_rbbox:
rbbox_feats = self.shared_head_rbbox(rbbox_feats)
rcls_score, rbbox_pred = self.rbbox_head(rbbox_feats)
rbbox_label = rcls_score.argmax(dim=1)
rrois = self.rbbox_head.regress_by_class_rbbox(rrois, rbbox_label,
rbbox_pred, img_meta[0])
rbbox_feats = self.vertex_roi_extractor(
x[:len(self.vertex_roi_extractor.featmap_strides)], rrois)
if self.with_shared_head_rbbox:
rbbox_feats = self.shared_head_rbbox(rbbox_feats)
rcls_score, rbbox_pred = self.vertex_head(rbbox_feats)
det_rbboxes, det_labels = self.vertex_head.get_det_rbboxes(
rrois,
rcls_score,
rbbox_pred,
img_shape,
scale_factor,
rescale=rescale,
cfg=rcnn_test_cfg)
rbbox_results = dbbox2result(det_rbboxes, det_labels,
self.rbbox_head.num_classes)
return rbbox_results
def simple_test(self, img, img_meta, proposals=None, rescale=False):
x = self.extract_feat(img)
proposal_list = self.simple_test_rpn(
x, img_meta, self.test_cfg.rpn) if proposals is None else proposals
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
rcnn_test_cfg = self.test_cfg.rcnn
rois = bbox2roi(proposal_list)
bbox_feats = self.bbox_roi_extractor(
x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = self.bbox_head(bbox_feats)
det_rbboxes, det_labels = self.bbox_head.get_det_bboxes(
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=rescale,
cfg=rcnn_test_cfg)
rbbox_results = dbbox2result(det_rbboxes, det_labels,
self.bbox_head.num_classes)
return rbbox_results
def simple_test(self, img, img_meta, proposals=None, rescale=False):
"""Test without augmentation."""
assert self.with_bbox, "Bbox head must be implemented."
assert self.with_rbbox, "RBox head must be implemented."
x = self.extract_feat(img)
proposal_list = self.simple_test_rpn(
x, img_meta, self.test_cfg.rpn) if proposals is None else proposals
det_bboxes, det_labels = self.simple_test_bboxes(x,
img_meta,
proposal_list,
self.test_cfg.rcnn,
rescale=rescale)
# TODO: implement the dbbox2result
# bbox_results = dbbox2result(det_bboxes, det_labels,
# self.bbox_head.num_classes)
bbox_results = bbox2result(det_bboxes, det_labels,
self.bbox_head.num_classes)
if not self.with_rbbox:
return bbox_results
else:
det_rbboxes, det_rlabels = self.simple_test_rbboxes_v2(
x, img_meta, det_bboxes, self.test_cfg.rrcnn, rescale=rescale)
# import pdb
# pdb.set_trace()
rbbox_results = dbbox2result(det_rbboxes, det_rlabels,
self.rbbox_head.num_classes)
return bbox_results, rbbox_results
def simple_test(self, img, img_meta, rescale=False):
# TODO: make if more flexible to add hbb and obb
x = self.extract_feat(img)
rbbox_outs = self.rbbox_head(x)
rbbox_inputs = rbbox_outs + (img_meta, self.test_cfg, rescale)
rbbox_list = self.rbbox_head.get_bboxes(*rbbox_inputs)
rbbox_results = [
dbbox2result(det_rbboxes, det_labels, self.rbbox_head.num_classes)
for det_rbboxes, det_labels in rbbox_list
]
return rbbox_results[0]
def aug_test(self, imgs, img_metas, proposals=None, rescale=None):
# raise NotImplementedError
# import pdb; pdb.set_trace()
proposal_list = self.aug_test_rpn_rotate(
self.extract_feats(imgs), img_metas, self.test_cfg.rpn)
rcnn_test_cfg = self.test_cfg.rcnn
aug_rbboxes = []
aug_rscores = []
for x, img_meta in zip(self.extract_feats(imgs), img_metas):
# only one image in the batch
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip)
angle = img_meta[0]['angle']
# print('img shape: ', img_shape)
if angle != 0:
try:
proposals = bbox_rotate_mapping(proposal_list[0][:, :4], img_shape,
angle)
except:
import pdb; pdb.set_trace()
rois = bbox2roi([proposals])
# recompute feature maps to save GPU memory
roi_feats = self.bbox_roi_extractor(
x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
roi_feats = self.shared_head(roi_feats)
cls_score, bbox_pred = self.bbox_head(roi_feats)
bbox_label = cls_score.argmax(dim=1)
rrois = self.bbox_head.regress_by_class_rbbox(roi2droi(rois), bbox_label,
bbox_pred,
img_meta[0])
rrois_enlarge = copy.deepcopy(rrois)
rrois_enlarge[:, 3] = rrois_enlarge[:, 3] * self.rbbox_roi_extractor.w_enlarge
rrois_enlarge[:, 4] = rrois_enlarge[:, 4] * self.rbbox_roi_extractor.h_enlarge
rbbox_feats = self.rbbox_roi_extractor(
x[:len(self.rbbox_roi_extractor.featmap_strides)], rrois_enlarge)
if self.with_shared_head_rbbox:
rbbox_feats = self.shared_head_rbbox(rbbox_feats)
rcls_score, rbbox_pred = self.rbbox_head(rbbox_feats)
rbboxes, rscores = self.rbbox_head.get_det_rbboxes(
rrois,
rcls_score,
rbbox_pred,
img_shape,
scale_factor,
rescale=rescale,
cfg=None)
aug_rbboxes.append(rbboxes)
aug_rscores.append(rscores)
merged_rbboxes, merged_rscores = merge_rotate_aug_bboxes(
aug_rbboxes, aug_rscores, img_metas, rcnn_test_cfg
)
det_rbboxes, det_rlabels = multiclass_nms_rbbox(
merged_rbboxes, merged_rscores, rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms, rcnn_test_cfg.max_per_img)
if rescale:
_det_rbboxes = det_rbboxes
else:
_det_rbboxes = det_rbboxes.clone()
_det_rbboxes[:, :4] *= img_metas[0][0]['scale_factor']
rbbox_results = dbbox2result(_det_rbboxes, det_rlabels,
self.rbbox_head.num_classes)
return rbbox_results
def simple_test(self, img, img_meta, proposals=None, rescale=False):
x = self.extract_feat(img)
proposal_list = self.simple_test_rpn(
x, img_meta, self.test_cfg.rpn) if proposals is None else proposals
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
rcnn_test_cfg = self.test_cfg.rcnn
ms_bbox_result = {}
ms_segm_result = {}
ms_scores = []
bbox_pred = []
rois = bbox2roi(proposal_list)
for i in range(self.num_stages):
bbox_roi_extractor = self.bbox_roi_extractor[i]
bbox_head = self.bbox_head[i]
bbox_feats = bbox_roi_extractor(
x[:len(bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = bbox_head(bbox_feats)
ms_scores.append(cls_score)
# if self.test_cfg.keep_all_stages:
# det_bboxes, det_labels = bbox_head.get_det_bboxes(
# rois,
# cls_score,
# bbox_pred,
# img_shape,
# scale_factor,
# rescale=rescale,
# cfg=rcnn_test_cfg)
# bbox_result = bbox2result(det_bboxes, det_labels,
# bbox_head.num_classes)
# ms_bbox_result['stage{}'.format(i)] = bbox_result
if i < self.num_stages - 1:
bbox_label = cls_score.argmax(dim=1)
rois = bbox_head.regress_by_class(rois, bbox_label, bbox_pred,
img_meta[0])
cls_score = sum(ms_scores) / self.num_stages
bbox_head = self.bbox_head[-1]
bbox_label = cls_score.argmax(dim=1)
rrois = bbox_head.regress_by_class_rbbox(roi2droi(rois), bbox_label, bbox_pred,
img_meta[0])
rrois_enlarge = copy.deepcopy(rrois)
rrois_enlarge[:, 3] = rrois_enlarge[:, 3] * self.rbbox_roi_extractor.w_enlarge
rrois_enlarge[:, 4] = rrois_enlarge[:, 4] * self.rbbox_roi_extractor.h_enlarge
rbbox_feats = self.rbbox_roi_extractor(
x[:len(self.rbbox_roi_extractor.featmap_strides)], rrois_enlarge)
if self.with_shared_head_rbbox:
rbbox_feats = self.shared_head_rbbox(rbbox_feats)
rcls_score, rbbox_pred = self.rbbox_head(rbbox_feats)
det_rbboxes, det_labels = self.rbbox_head.get_det_rbboxes(
rrois,
rcls_score,
rbbox_pred,
img_shape,
scale_factor,
rescale=rescale,
cfg=rcnn_test_cfg)
rbbox_results = dbbox2result(det_rbboxes, det_labels,
self.rbbox_head.num_classes)
return rbbox_results
