class_docstr: This code is based on
  https://github.com/fcjian/TOOD/blob/master/mmdet/models/dense_heads/tood_head.py
class_docstr_tok: ["This","code","is","based","on","https",":","\/\/github.com\/fcjian\/TOOD\/blob\/master\/mmdet\/models\/dense_heads\/tood_head.py"]
code: def forward(self, feats):
      assert len(feats) == len(
          self.fpn_strides
      ), "The size of feats is not equal to size of fpn_strides"
  
      (
          anchors,
          anchor_points,
          num_anchors_list,
          stride_tensor,
      ) = generate_anchors_for_grid_cell(
          feats,
          self.fpn_strides,
          self.grid_cell_scale,
          self.grid_cell_offset,
      )
      anchor_centers_split = paddle.split(
          anchor_points / stride_tensor, num_anchors_list
      )
  
      cls_score_list, bbox_pred_list = [], []
      for feat, scale_reg, anchor_centers, stride in zip(
          feats,
          self.scales_regs,
          anchor_centers_split,
          self.fpn_strides,
      ):
          b, _, h, w = get_static_shape(feat)
          inter_feats = []
          for inter_conv in self.inter_convs:
              feat = F.relu(inter_conv(feat))
              inter_feats.append(feat)
          feat = paddle.concat(inter_feats, axis=1)
  
          # task decomposition
          avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
          cls_feat = self.cls_decomp(feat, avg_feat)
          reg_feat = self.reg_decomp(feat, avg_feat)
  
          # cls prediction and alignment
          cls_logits = self.tood_cls(cls_feat)
          if self.use_align_head:
              cls_prob = F.relu(self.cls_prob_conv1(feat))
              cls_prob = F.sigmoid(self.cls_prob_conv2(cls_prob))
              cls_score = (F.sigmoid(cls_logits) * cls_prob).sqrt()
          else:
              cls_score = F.sigmoid(cls_logits)
          cls_score_list.append(
              cls_score.flatten(2).transpose([0, 2, 1])
          )
  
          # reg prediction and alignment
          reg_dist = scale_reg(self.tood_reg(reg_feat).exp())
          reg_dist = reg_dist.flatten(2).transpose([0, 2, 1])
          reg_bbox = batch_distance2bbox(
              anchor_centers.unsqueeze(0), reg_dist
          )
          if self.use_align_head:
              reg_offset = F.relu(self.reg_offset_conv1(feat))
              reg_offset = self.reg_offset_conv2(reg_offset)
              reg_bbox = reg_bbox.transpose([0, 2, 1]).reshape(
                  [b, 4, h, w]
              )
              anchor_centers = anchor_centers.reshape([1, h, w, 2])
              bbox_pred = self._reg_grid_sample(
                  reg_bbox, reg_offset, anchor_centers
              )
              bbox_pred = bbox_pred.flatten(2).transpose([0, 2, 1])
          else:
              bbox_pred = reg_bbox
  
          if not self.training:
              bbox_pred *= stride
          bbox_pred_list.append(bbox_pred)
      cls_score_list = paddle.concat(cls_score_list, axis=1)
      bbox_pred_list = paddle.concat(bbox_pred_list, axis=1)
  
      return (
          cls_score_list,
          bbox_pred_list,
          anchors,
          num_anchors_list,
          stride_tensor,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Constant
  from ppdet.core.workspace import register
  from ..initializer import normal_, constant_, bias_init_with_prob
  from ppdet.modeling.bbox_utils import bbox_center, batch_distance2bbox
  from ..losses import GIoULoss
  from ppdet.modeling.layers import ConvNormLayer
  from ppdet.modeling.ops import get_static_shape
  from ppdet.modeling.assigners.utils import generate_anchors_for_grid_cell
label_desc: Use image node_id 5 for calling the TOODHead obj's underlying member method code with example usage: obj.forward(feats) and returns: cls_score_list,  bbox_pred_list,  anchors,  num_anchors_list,  stride_tensor