def _forward_train(self, image, im_info, gt_boxes):
loss_dict = {}
fpn_fms = self.FPN(image)
# fpn_fms stride: 64,32,16,8,4, p6->p2
rpn_rois, loss_dict_rpn = self.RPN(fpn_fms, im_info, gt_boxes)
rcnn_rois, rcnn_labels, rcnn_bbox_targets = fpn_roi_target(
rpn_rois, im_info, gt_boxes, top_k=1)
loss_dict_rcnn = self.RCNN(fpn_fms, rcnn_rois,
rcnn_labels, rcnn_bbox_targets)
loss_dict.update(loss_dict_rpn)
loss_dict.update(loss_dict_rcnn)
return loss_dict
def _forward_train(self, image, im_info, gt_boxes):
loss_dict = {}
fpn_fms = self.FPN(image)
# fpn_fms stride: 64,32,16,8,4, p6->p2
rpn_rois, loss_dict_rpn = self.RPN(fpn_fms, im_info, gt_boxes)
rcnn_rois, rcnn_labels, rcnn_bbox_targets = fpn_roi_target(
rpn_rois, im_info, gt_boxes, top_k=1)
proposals, loss_dict_cascade_0 = self.Cascade_0(
fpn_fms, rcnn_rois, rcnn_labels, rcnn_bbox_targets)
cascade_rois, cascade_labels, cascade_bbox_targets = cascade_roi_target(
proposals, im_info, gt_boxes, pos_threshold=0.6, top_k=2)
#proposals, loss_dict_cascade_1 = self.Cascade_1(
# fpn_fms, cascade_rois, cascade_labels, cascade_bbox_targets)
#cascade_rois, cascade_labels, cascade_bbox_targets = cascade_roi_target(
# proposals, im_info, gt_boxes, pos_threshold=0.7, top_k=2)
loss_dict_rcnn = self.RCNN(fpn_fms, cascade_rois,
cascade_labels, cascade_bbox_targets)
loss_dict.update(loss_dict_rpn)
loss_dict.update(loss_dict_cascade_0)
#loss_dict.update(loss_dict_cascade_1)
loss_dict.update(loss_dict_rcnn)
return loss_dict