class_docstr: FCOSR Head, refer to https://arxiv.org/abs/2111.10780 for details 
class_docstr_tok: ["FCOSR","Head",",","refer","to","https",":","\/\/arxiv.org\/abs\/2111.10780","for","details"]
code: def forward_eval(self, feats, target=None):
      cls_pred_list, reg_pred_list = [], []
      anchor_points, _, _ = self._generate_anchors(feats)
      for stride, feat, scale in zip(
          self.fpn_strides, feats, self.scales
      ):
          b, _, h, w = paddle.shape(feat)
          # cls
          cls_feat = feat
          for cls_layer in self.stem_cls:
              cls_feat = cls_layer(cls_feat)
          cls_pred = F.sigmoid(self.pred_cls(cls_feat))
          cls_pred_list.append(
              cls_pred.reshape([b, self.num_classes, h * w])
          )
          # reg
          reg_feat = feat
          for reg_layer in self.stem_reg:
              reg_feat = reg_layer(reg_feat)
  
          reg_xy = scale(self.pred_xy(reg_feat)) * stride
          reg_wh = F.elu(scale(self.pred_wh(reg_feat)) + 1.0) * stride
          reg_angle = self.pred_angle(reg_feat)
          reg_angle = fmod_eval(reg_angle, self.half_pi)
          reg_pred = paddle.concat([reg_xy, reg_wh, reg_angle], axis=1)
          reg_pred = reg_pred.reshape([b, 5, h * w]).transpose(
              (0, 2, 1)
          )
          reg_pred_list.append(reg_pred)
  
      cls_pred_list = paddle.concat(cls_pred_list, axis=2)
      reg_pred_list = paddle.concat(reg_pred_list, axis=1)
      reg_pred_list = self._bbox_decode(anchor_points, reg_pred_list)
      return cls_pred_list, reg_pred_list
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from .fcos_head import ScaleReg
  from ..initializer import bias_init_with_prob, constant_, normal_
  from ..ops import get_act_fn, anchor_generator
  from ..rbox_utils import box2corners
  from ..losses import ProbIoULoss
  import numpy
label_desc: Use image node_id 7 for calling the FCOSRHead obj's underlying member method code with example usage: obj.forward_eval(feats, target) and returns: cls_pred_list,  reg_pred_list