def show_train_res(self, img, gt_bboxes, img_metas, outs, score_threshold):
from configs.common import DIM_PARSE
img = img[0].permute(1,2,0).cpu().data.numpy()
_gt_bboxes = [g.cpu().data.numpy() for g in gt_bboxes][0:1]
rescale = False
bbox_inputs = outs + (img_metas, self.test_cfg, rescale)
bbox_list = self.bbox_head.get_bboxes(*bbox_inputs)
det_bboxes, det_labels, _ = bbox_list[0]
_det_bboxes0 = det_bboxes.cpu().data.numpy()
assert not 'background' in img_metas[0]['classes']
dim_parse = DIM_PARSE( self.obj_rep, len(img_metas[0]['classes'])+1 )
_det_bboxes1 = dim_parse.clean_bboxes_out(_det_bboxes0,'final', 'line_ave' )
_det_bboxes = [_det_bboxes1]
ngt = len(_gt_bboxes[0])
ndt = len(_det_bboxes[0])
print(f'gt num={ngt}, det num={ndt}')
mask0 = _det_bboxes[0][:,5] >= score_threshold[0]
mask1 = _det_bboxes[0][:,5] <= score_threshold[1]
mask = mask0 * mask1
#debug_utils._show_lines_ls_points_ls((512,512), _det_bboxes)
#debug_utils._show_lines_ls_points_ls((512,512), _gt_bboxes)
_show_objs_ls_points_ls((512,512),
objs_ls = [_gt_bboxes[0][:,:5], _det_bboxes[0][mask][:,:5]],
obj_scores_ls = [None, _det_bboxes[0][mask][:,5]],
obj_colors=['red','green'], obj_rep=self.obj_rep )
_show_objs_ls_points_ls(img[:,:,0], [_gt_bboxes[0]], obj_rep=self.obj_rep)
pass
def simple_test(self, img, img_meta, rescale=False, gt_bboxes=None, gt_labels=None, gt_relations=None):
from configs.common import DIM_PARSE
record_time = DEBUG_CFG.RECORD_TEST_TIME
if record_time:
t0 = time.time()
if DEBUG_CFG.DISABLE_RESCALE:
rescale = False
#_show_objs_ls_points_ls(img[0].permute(1,2,0).cpu().data.numpy(), [gt_bboxes[0][0].cpu().data.numpy()], 'RoLine2D_UpRight_xyxy_sin2a')
x = self.extract_feat(img, gt_bboxes)
if record_time:
t1 = time.time()
self.update_dynamic_shape(x, img_meta)
#update_img_shape_for_pcl(x, img_meta[0], self.point_strides)
outs = self.bbox_head(x)
if record_time:
t2 = time.time()
bbox_inputs = outs + (img_meta, self.test_cfg, rescale)
bbox_list = self.bbox_head.get_bboxes(*bbox_inputs)
bbox_results = [
bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
for det_bboxes, det_labels ,_ in bbox_list
]
if bbox_list[0][2] is not None:
relation_scores = [self.bbox_head.get_relations(relation_scores).cpu().numpy() for _,_, relation_scores in bbox_list]
else:
relation_scores = [None]
results = dict( det_bboxes=bbox_results[0], gt_bboxes=gt_bboxes, gt_labels=gt_labels, img = img, det_relations = relation_scores[0])
if record_time:
t3 = time.time()
print(f'\nTime\nbackbone:\t {t1-t0:.4f}')
print(f'head prediction:\t {t2-t1:.4f}')
print(f'NMS: {t3-t2:.4f}')
pass
if 0:
dim_parse = DIM_PARSE( len(img_meta[0]['classes'])+1 )
det_bboxes = dim_parse.clean_bboxes_out( bbox_results[0][0],'final', 'line_ave' )[:,:5]
_show_objs_ls_points_ls(img[0].permute(1,2,0).cpu().data.numpy(), [gt_bboxes[0][0].cpu().data.numpy(), det_bboxes], 'RoLine2D_UpRight_xyxy_sin2a')
_show_objs_ls_points_ls(img[0].permute(1,2,0).cpu().data.numpy(), [gt_bboxes[0][0].cpu().data.numpy(), ], 'RoLine2D_UpRight_xyxy_sin2a')
return results