def simple_test_bboxes(self,
x,
img_metas,
proposals,
rcnn_test_cfg,
rescale=False):
"""Test only det bboxes without augmentation."""
rois = bbox2roi(proposals)
bbox_results = self._bbox_forward(x, rois, img_metas)
cls_score = bbox_results['cls_score']
img_shape = img_metas[0]['img_shape']
crop_quadruple = np.array([0, 0, 1, 1])
flip = False
if 'crop_quadruple' in img_metas[0]:
crop_quadruple = img_metas[0]['crop_quadruple']
if 'flip' in img_metas[0]:
flip = img_metas[0]['flip']
det_bboxes, det_labels = self.bbox_head.get_det_bboxes(
rois,
cls_score,
img_shape,
flip=flip,
crop_quadruple=crop_quadruple,
cfg=rcnn_test_cfg)
return det_bboxes, det_labels
def _bbox_forward_train(self, x, sampling_results, gt_bboxes,
gt_labels, img_metas):
"""Run forward function and calculate loss for box head in
training."""
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(x, rois, img_metas)
bbox_targets = self.bbox_head.get_targets(sampling_results,
gt_bboxes, gt_labels,
self.train_cfg)
loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
bbox_results['bbox_pred'], rois,
*bbox_targets)
bbox_results.update(loss_bbox=loss_bbox)
return bbox_results