code: # Copyright (c) OpenMMLab. All rights reserved.
  import numpy as np
  import torch
  import torch.nn as nn
  from mmcv.cnn import ConvModule
  from mmcv.ops import DeformConv2d
  
  from mmdet.core import (build_assigner, build_sampler, images_to_levels,
                          multi_apply, unmap)
  from mmdet.core.anchor.point_generator import MlvlPointGenerator
  from mmdet.core.utils import filter_scores_and_topk
  from ..builder import HEADS, build_loss
  from .anchor_free_head import AnchorFreeHead
  
  
  @HEADS.register_module()
  class RepPointsHead(AnchorFreeHead):
      """RepPoint head.
  
      Args:
          point_feat_channels (int): Number of channels of points features.
          gradient_mul (float): The multiplier to gradients from
              points refinement and recognition.
          point_strides (Iterable): points strides.
          point_base_scale (int): bbox scale for assigning labels.
          loss_cls (dict): Config of classification loss.
          loss_bbox_init (dict): Config of initial points loss.
          loss_bbox_refine (dict): Config of points loss in refinement.
          use_grid_points (bool): If we use bounding box representation, the
          reppoints is represented as grid points on the bounding box.
          center_init (bool): Whether to use center point assignment.
          transform_method (str): The methods to transform RepPoints to bbox.
          init_cfg (dict or list[dict], optional): Initialization config dict.
      """  # noqa: W605
  
      def __init__(self,
                   num_classes,
                   in_channels,
                   point_feat_channels=256,
                   num_points=9,
                   gradient_mul=0.1,
                   point_strides=[8, 16, 32, 64, 128],
                   point_base_scale=4,
                   loss_cls=dict(
                       type='FocalLoss',
                       use_sigmoid=True,
                       gamma=2.0,
                       alpha=0.25,
                       loss_weight=1.0),
                   loss_bbox_init=dict(
                       type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.5),
                   loss_bbox_refine=dict(
                       type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
                   use_grid_points=False,
                   center_init=True,
                   transform_method='moment',
                   moment_mul=0.01,
                   init_cfg=dict(
                       type='Normal',
                       layer='Conv2d',
                       std=0.01,
                       override=dict(
                           type='Normal',
                           name='reppoints_cls_out',
                           std=0.01,
                           bias_prob=0.01)),
                   **kwargs):
          self.num_points = num_points
          self.point_feat_channels = point_feat_channels
          self.use_grid_points = use_grid_points
          self.center_init = center_init
  
          # we use deform conv to extract points features
          self.dcn_kernel = int(np.sqrt(num_points))
          self.dcn_pad = int((self.dcn_kernel - 1) / 2)
          assert self.dcn_kernel * self.dcn_kernel == num_points, \
              'The points number should be a square number.'
          assert self.dcn_kernel % 2 == 1, \
              'The points number should be an odd square number.'
          dcn_base = np.arange(-self.dcn_pad,
                               self.dcn_pad + 1).astype(np.float64)
          dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
          dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
          dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
              (-1))
          self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
  
          super().__init__(
              num_classes,
              in_channels,
              loss_cls=loss_cls,
              init_cfg=init_cfg,
              **kwargs)
  
          self.gradient_mul = gradient_mul
          self.point_base_scale = point_base_scale
          self.point_strides = point_strides
          self.prior_generator = MlvlPointGenerator(
              self.point_strides, offset=0.)
  
          self.sampling = loss_cls['type'] not in ['FocalLoss']
          if self.train_cfg:
              self.init_assigner = build_assigner(self.train_cfg.init.assigner)
              self.refine_assigner = build_assigner(
                  self.train_cfg.refine.assigner)
              # use PseudoSampler when sampling is False
              if self.sampling and hasattr(self.train_cfg, 'sampler'):
                  sampler_cfg = self.train_cfg.sampler
              else:
                  sampler_cfg = dict(type='PseudoSampler')
              self.sampler = build_sampler(sampler_cfg, context=self)
          self.transform_method = transform_method
          if self.transform_method == 'moment':
              self.moment_transfer = nn.Parameter(
                  data=torch.zeros(2), requires_grad=True)
              self.moment_mul = moment_mul
  
          self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
          if self.use_sigmoid_cls:
              self.cls_out_channels = self.num_classes
          else:
              self.cls_out_channels = self.num_classes + 1
          self.loss_bbox_init = build_loss(loss_bbox_init)
          self.loss_bbox_refine = build_loss(loss_bbox_refine)
  
      def _init_layers(self):
          """Initialize layers of the head."""
          self.relu = nn.ReLU(inplace=True)
          self.cls_convs = nn.ModuleList()
          self.reg_convs = nn.ModuleList()
          for i in range(self.stacked_convs):
              chn = self.in_channels if i == 0 else self.feat_channels
              self.cls_convs.append(
                  ConvModule(
                      chn,
                      self.feat_channels,
                      3,
                      stride=1,
                      padding=1,
                      conv_cfg=self.conv_cfg,
                      norm_cfg=self.norm_cfg))
              self.reg_convs.append(
                  ConvModule(
                      chn,
                      self.feat_channels,
                      3,
                      stride=1,
                      padding=1,
                      conv_cfg=self.conv_cfg,
                      norm_cfg=self.norm_cfg))
          pts_out_dim = 4 if self.use_grid_points else 2 * self.num_points
          self.reppoints_cls_conv = DeformConv2d(self.feat_channels,
                                                 self.point_feat_channels,
                                                 self.dcn_kernel, 1,
                                                 self.dcn_pad)
          self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels,
                                             self.cls_out_channels, 1, 1, 0)
          self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels,
                                                   self.point_feat_channels, 3,
                                                   1, 1)
          self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels,
                                                  pts_out_dim, 1, 1, 0)
          self.reppoints_pts_refine_conv = DeformConv2d(self.feat_channels,
                                                        self.point_feat_channels,
                                                        self.dcn_kernel, 1,
                                                        self.dcn_pad)
          self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels,
                                                    pts_out_dim, 1, 1, 0)
  
      def points2bbox(self, pts, y_first=True):
          """Converting the points set into bounding box.
  
          :param pts: the input points sets (fields), each points
              set (fields) is represented as 2n scalar.
          :param y_first: if y_first=True, the point set is represented as
              [y1, x1, y2, x2 ... yn, xn], otherwise the point set is
              represented as [x1, y1, x2, y2 ... xn, yn].
          :return: each points set is converting to a bbox [x1, y1, x2, y2].
          """
          pts_reshape = pts.view(pts.shape[0], -1, 2, *pts.shape[2:])
          pts_y = pts_reshape[:, :, 0, ...] if y_first else pts_reshape[:, :, 1,
                                                                        ...]
          pts_x = pts_reshape[:, :, 1, ...] if y_first else pts_reshape[:, :, 0,
                                                                        ...]
          if self.transform_method == 'minmax':
              bbox_left = pts_x.min(dim=1, keepdim=True)[0]
              bbox_right = pts_x.max(dim=1, keepdim=True)[0]
              bbox_up = pts_y.min(dim=1, keepdim=True)[0]
              bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
              bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
                               dim=1)
          elif self.transform_method == 'partial_minmax':
              pts_y = pts_y[:, :4, ...]
              pts_x = pts_x[:, :4, ...]
              bbox_left = pts_x.min(dim=1, keepdim=True)[0]
              bbox_right = pts_x.max(dim=1, keepdim=True)[0]
              bbox_up = pts_y.min(dim=1, keepdim=True)[0]
              bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
              bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
                               dim=1)
          elif self.transform_method == 'moment':
              pts_y_mean = pts_y.mean(dim=1, keepdim=True)
              pts_x_mean = pts_x.mean(dim=1, keepdim=True)
              pts_y_std = torch.std(pts_y - pts_y_mean, dim=1, keepdim=True)
              pts_x_std = torch.std(pts_x - pts_x_mean, dim=1, keepdim=True)
              moment_transfer = (self.moment_transfer * self.moment_mul) + (
                  self.moment_transfer.detach() * (1 - self.moment_mul))
              moment_width_transfer = moment_transfer[0]
              moment_height_transfer = moment_transfer[1]
              half_width = pts_x_std * torch.exp(moment_width_transfer)
              half_height = pts_y_std * torch.exp(moment_height_transfer)
              bbox = torch.cat([
                  pts_x_mean - half_width, pts_y_mean - half_height,
                  pts_x_mean + half_width, pts_y_mean + half_height
              ],
                               dim=1)
          else:
              raise NotImplementedError
          return bbox
  
      def gen_grid_from_reg(self, reg, previous_boxes):
          """Base on the previous bboxes and regression values, we compute the
          regressed bboxes and generate the grids on the bboxes.
  
          :param reg: the regression value to previous bboxes.
          :param previous_boxes: previous bboxes.
          :return: generate grids on the regressed bboxes.
          """
          b, _, h, w = reg.shape
          bxy = (previous_boxes[:, :2, ...] + previous_boxes[:, 2:, ...]) / 2.
          bwh = (previous_boxes[:, 2:, ...] -
                 previous_boxes[:, :2, ...]).clamp(min=1e-6)
          grid_topleft = bxy + bwh * reg[:, :2, ...] - 0.5 * bwh * torch.exp(
              reg[:, 2:, ...])
          grid_wh = bwh * torch.exp(reg[:, 2:, ...])
          grid_left = grid_topleft[:, [0], ...]
          grid_top = grid_topleft[:, [1], ...]
          grid_width = grid_wh[:, [0], ...]
          grid_height = grid_wh[:, [1], ...]
          intervel = torch.linspace(0., 1., self.dcn_kernel).view(
              1, self.dcn_kernel, 1, 1).type_as(reg)
          grid_x = grid_left + grid_width * intervel
          grid_x = grid_x.unsqueeze(1).repeat(1, self.dcn_kernel, 1, 1, 1)
          grid_x = grid_x.view(b, -1, h, w)
          grid_y = grid_top + grid_height * intervel
          grid_y = grid_y.unsqueeze(2).repeat(1, 1, self.dcn_kernel, 1, 1)
          grid_y = grid_y.view(b, -1, h, w)
          grid_yx = torch.stack([grid_y, grid_x], dim=2)
          grid_yx = grid_yx.view(b, -1, h, w)
          regressed_bbox = torch.cat([
              grid_left, grid_top, grid_left + grid_width, grid_top + grid_height
          ], 1)
          return grid_yx, regressed_bbox
  
      def forward(self, feats):
          return multi_apply(self.forward_single, feats)
  
      def forward_single(self, x):
          """Forward feature map of a single FPN level."""
          dcn_base_offset = self.dcn_base_offset.type_as(x)
          # If we use center_init, the initial reppoints is from center points.
          # If we use bounding bbox representation, the initial reppoints is
          #   from regular grid placed on a pre-defined bbox.
          if self.use_grid_points or not self.center_init:
              scale = self.point_base_scale / 2
              points_init = dcn_base_offset / dcn_base_offset.max() * scale
              bbox_init = x.new_tensor([-scale, -scale, scale,
                                        scale]).view(1, 4, 1, 1)
          else:
              points_init = 0
          cls_feat = x
          pts_feat = x
          for cls_conv in self.cls_convs:
              cls_feat = cls_conv(cls_feat)
          for reg_conv in self.reg_convs:
              pts_feat = reg_conv(pts_feat)
          # initialize reppoints
          pts_out_init = self.reppoints_pts_init_out(
              self.relu(self.reppoints_pts_init_conv(pts_feat)))
          if self.use_grid_points:
              pts_out_init, bbox_out_init = self.gen_grid_from_reg(
                  pts_out_init, bbox_init.detach())
          else:
              pts_out_init = pts_out_init + points_init
          # refine and classify reppoints
          pts_out_init_grad_mul = (1 - self.gradient_mul) * pts_out_init.detach(
          ) + self.gradient_mul * pts_out_init
          dcn_offset = pts_out_init_grad_mul - dcn_base_offset
          cls_out = self.reppoints_cls_out(
              self.relu(self.reppoints_cls_conv(cls_feat, dcn_offset)))
          pts_out_refine = self.reppoints_pts_refine_out(
              self.relu(self.reppoints_pts_refine_conv(pts_feat, dcn_offset)))
          if self.use_grid_points:
              pts_out_refine, bbox_out_refine = self.gen_grid_from_reg(
                  pts_out_refine, bbox_out_init.detach())
          else:
              pts_out_refine = pts_out_refine + pts_out_init.detach()
  
          if self.training:
              return cls_out, pts_out_init, pts_out_refine
          else:
              return cls_out, self.points2bbox(pts_out_refine)
  
      def get_points(self, featmap_sizes, img_metas, device):
          """Get points according to feature map sizes.
  
          Args:
              featmap_sizes (list[tuple]): Multi-level feature map sizes.
              img_metas (list[dict]): Image meta info.
  
          Returns:
              tuple: points of each image, valid flags of each image
          """
          num_imgs = len(img_metas)
  
          # since feature map sizes of all images are the same, we only compute
          # points center for one time
          multi_level_points = self.prior_generator.grid_priors(
              featmap_sizes, device=device, with_stride=True)
          points_list = [[point.clone() for point in multi_level_points]
                         for _ in range(num_imgs)]
  
          # for each image, we compute valid flags of multi level grids
          valid_flag_list = []
          for img_id, img_meta in enumerate(img_metas):
              multi_level_flags = self.prior_generator.valid_flags(
                  featmap_sizes, img_meta['pad_shape'])
              valid_flag_list.append(multi_level_flags)
  
          return points_list, valid_flag_list
  
      def centers_to_bboxes(self, point_list):
          """Get bboxes according to center points.
  
          Only used in :class:`MaxIoUAssigner`.
          """
          bbox_list = []
          for i_img, point in enumerate(point_list):
              bbox = []
              for i_lvl in range(len(self.point_strides)):
                  scale = self.point_base_scale * self.point_strides[i_lvl] * 0.5
                  bbox_shift = torch.Tensor([-scale, -scale, scale,
                                             scale]).view(1, 4).type_as(point[0])
                  bbox_center = torch.cat(
                      [point[i_lvl][:, :2], point[i_lvl][:, :2]], dim=1)
                  bbox.append(bbox_center + bbox_shift)
              bbox_list.append(bbox)
          return bbox_list
  
      def offset_to_pts(self, center_list, pred_list):
          """Change from point offset to point coordinate."""
          pts_list = []
          for i_lvl in range(len(self.point_strides)):
              pts_lvl = []
              for i_img in range(len(center_list)):
                  pts_center = center_list[i_img][i_lvl][:, :2].repeat(
                      1, self.num_points)
                  pts_shift = pred_list[i_lvl][i_img]
                  yx_pts_shift = pts_shift.permute(1, 2, 0).view(
                      -1, 2 * self.num_points)
                  y_pts_shift = yx_pts_shift[..., 0::2]
                  x_pts_shift = yx_pts_shift[..., 1::2]
                  xy_pts_shift = torch.stack([x_pts_shift, y_pts_shift], -1)
                  xy_pts_shift = xy_pts_shift.view(*yx_pts_shift.shape[:-1], -1)
                  pts = xy_pts_shift * self.point_strides[i_lvl] + pts_center
                  pts_lvl.append(pts)
              pts_lvl = torch.stack(pts_lvl, 0)
              pts_list.append(pts_lvl)
          return pts_list
  
      def _point_target_single(self,
                               flat_proposals,
                               valid_flags,
                               gt_bboxes,
                               gt_bboxes_ignore,
                               gt_labels,
                               stage='init',
                               unmap_outputs=True):
          inside_flags = valid_flags
          if not inside_flags.any():
              return (None, ) * 7
          # assign gt and sample proposals
          proposals = flat_proposals[inside_flags, :]
  
          if stage == 'init':
              assigner = self.init_assigner
              pos_weight = self.train_cfg.init.pos_weight
          else:
              assigner = self.refine_assigner
              pos_weight = self.train_cfg.refine.pos_weight
          assign_result = assigner.assign(proposals, gt_bboxes, gt_bboxes_ignore,
                                          None if self.sampling else gt_labels)
          sampling_result = self.sampler.sample(assign_result, proposals,
                                                gt_bboxes)
  
          num_valid_proposals = proposals.shape[0]
          bbox_gt = proposals.new_zeros([num_valid_proposals, 4])
          pos_proposals = torch.zeros_like(proposals)
          proposals_weights = proposals.new_zeros([num_valid_proposals, 4])
          labels = proposals.new_full((num_valid_proposals, ),
                                      self.num_classes,
                                      dtype=torch.long)
          label_weights = proposals.new_zeros(
              num_valid_proposals, dtype=torch.float)
  
          pos_inds = sampling_result.pos_inds
          neg_inds = sampling_result.neg_inds
          if len(pos_inds) > 0:
              pos_gt_bboxes = sampling_result.pos_gt_bboxes
              bbox_gt[pos_inds, :] = pos_gt_bboxes
              pos_proposals[pos_inds, :] = proposals[pos_inds, :]
              proposals_weights[pos_inds, :] = 1.0
              if gt_labels is None:
                  # Only rpn gives gt_labels as None
                  # Foreground is the first class
                  labels[pos_inds] = 0
              else:
                  labels[pos_inds] = gt_labels[
                      sampling_result.pos_assigned_gt_inds]
              if pos_weight <= 0:
                  label_weights[pos_inds] = 1.0
              else:
                  label_weights[pos_inds] = pos_weight
          if len(neg_inds) > 0:
              label_weights[neg_inds] = 1.0
  
          # map up to original set of proposals
          if unmap_outputs:
              num_total_proposals = flat_proposals.size(0)
              labels = unmap(labels, num_total_proposals, inside_flags)
              label_weights = unmap(label_weights, num_total_proposals,
                                    inside_flags)
              bbox_gt = unmap(bbox_gt, num_total_proposals, inside_flags)
              pos_proposals = unmap(pos_proposals, num_total_proposals,
                                    inside_flags)
              proposals_weights = unmap(proposals_weights, num_total_proposals,
                                        inside_flags)
  
          return (labels, label_weights, bbox_gt, pos_proposals,
                  proposals_weights, pos_inds, neg_inds)
  
      def get_targets(self,
                      proposals_list,
                      valid_flag_list,
                      gt_bboxes_list,
                      img_metas,
                      gt_bboxes_ignore_list=None,
                      gt_labels_list=None,
                      stage='init',
                      label_channels=1,
                      unmap_outputs=True):
          """Compute corresponding GT box and classification targets for
          proposals.
  
          Args:
              proposals_list (list[list]): Multi level points/bboxes of each
                  image.
              valid_flag_list (list[list]): Multi level valid flags of each
                  image.
              gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
              img_metas (list[dict]): Meta info of each image.
              gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
                  ignored.
              gt_bboxes_list (list[Tensor]): Ground truth labels of each box.
              stage (str): `init` or `refine`. Generate target for init stage or
                  refine stage
              label_channels (int): Channel of label.
              unmap_outputs (bool): Whether to map outputs back to the original
                  set of anchors.
  
          Returns:
              tuple:
                  - labels_list (list[Tensor]): Labels of each level.
                  - label_weights_list (list[Tensor]): Label weights of each level.  # noqa: E501
                  - bbox_gt_list (list[Tensor]): Ground truth bbox of each level.
                  - proposal_list (list[Tensor]): Proposals(points/bboxes) of each level.  # noqa: E501
                  - proposal_weights_list (list[Tensor]): Proposal weights of each level.  # noqa: E501
                  - num_total_pos (int): Number of positive samples in all images.  # noqa: E501
                  - num_total_neg (int): Number of negative samples in all images.  # noqa: E501
          """
          assert stage in ['init', 'refine']
          num_imgs = len(img_metas)
          assert len(proposals_list) == len(valid_flag_list) == num_imgs
  
          # points number of multi levels
          num_level_proposals = [points.size(0) for points in proposals_list[0]]
  
          # concat all level points and flags to a single tensor
          for i in range(num_imgs):
              assert len(proposals_list[i]) == len(valid_flag_list[i])
              proposals_list[i] = torch.cat(proposals_list[i])
              valid_flag_list[i] = torch.cat(valid_flag_list[i])
  
          # compute targets for each image
          if gt_bboxes_ignore_list is None:
              gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
          if gt_labels_list is None:
              gt_labels_list = [None for _ in range(num_imgs)]
          (all_labels, all_label_weights, all_bbox_gt, all_proposals,
           all_proposal_weights, pos_inds_list, neg_inds_list) = multi_apply(
               self._point_target_single,
               proposals_list,
               valid_flag_list,
               gt_bboxes_list,
               gt_bboxes_ignore_list,
               gt_labels_list,
               stage=stage,
               unmap_outputs=unmap_outputs)
          # no valid points
          if any([labels is None for labels in all_labels]):
              return None
          # sampled points of all images
          num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
          num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
          labels_list = images_to_levels(all_labels, num_level_proposals)
          label_weights_list = images_to_levels(all_label_weights,
                                                num_level_proposals)
          bbox_gt_list = images_to_levels(all_bbox_gt, num_level_proposals)
          proposals_list = images_to_levels(all_proposals, num_level_proposals)
          proposal_weights_list = images_to_levels(all_proposal_weights,
                                                   num_level_proposals)
          return (labels_list, label_weights_list, bbox_gt_list, proposals_list,
                  proposal_weights_list, num_total_pos, num_total_neg)
  
      def loss_single(self, cls_score, pts_pred_init, pts_pred_refine, labels,
                      label_weights, bbox_gt_init, bbox_weights_init,
                      bbox_gt_refine, bbox_weights_refine, stride,
                      num_total_samples_init, num_total_samples_refine):
          # classification loss
          labels = labels.reshape(-1)
          label_weights = label_weights.reshape(-1)
          cls_score = cls_score.permute(0, 2, 3,
                                        1).reshape(-1, self.cls_out_channels)
          cls_score = cls_score.contiguous()
          loss_cls = self.loss_cls(
              cls_score,
              labels,
              label_weights,
              avg_factor=num_total_samples_refine)
  
          # points loss
          bbox_gt_init = bbox_gt_init.reshape(-1, 4)
          bbox_weights_init = bbox_weights_init.reshape(-1, 4)
          bbox_pred_init = self.points2bbox(
              pts_pred_init.reshape(-1, 2 * self.num_points), y_first=False)
          bbox_gt_refine = bbox_gt_refine.reshape(-1, 4)
          bbox_weights_refine = bbox_weights_refine.reshape(-1, 4)
          bbox_pred_refine = self.points2bbox(
              pts_pred_refine.reshape(-1, 2 * self.num_points), y_first=False)
          normalize_term = self.point_base_scale * stride
          loss_pts_init = self.loss_bbox_init(
              bbox_pred_init / normalize_term,
              bbox_gt_init / normalize_term,
              bbox_weights_init,
              avg_factor=num_total_samples_init)
          loss_pts_refine = self.loss_bbox_refine(
              bbox_pred_refine / normalize_term,
              bbox_gt_refine / normalize_term,
              bbox_weights_refine,
              avg_factor=num_total_samples_refine)
          return loss_cls, loss_pts_init, loss_pts_refine
  
      def loss(self,
               cls_scores,
               pts_preds_init,
               pts_preds_refine,
               gt_bboxes,
               gt_labels,
               img_metas,
               gt_bboxes_ignore=None):
          featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
          device = cls_scores[0].device
          label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
  
          # target for initial stage
          center_list, valid_flag_list = self.get_points(featmap_sizes,
                                                         img_metas, device)
          pts_coordinate_preds_init = self.offset_to_pts(center_list,
                                                         pts_preds_init)
          if self.train_cfg.init.assigner['type'] == 'PointAssigner':
              # Assign target for center list
              candidate_list = center_list
          else:
              # transform center list to bbox list and
              #   assign target for bbox list
              bbox_list = self.centers_to_bboxes(center_list)
              candidate_list = bbox_list
          cls_reg_targets_init = self.get_targets(
              candidate_list,
              valid_flag_list,
              gt_bboxes,
              img_metas,
              gt_bboxes_ignore_list=gt_bboxes_ignore,
              gt_labels_list=gt_labels,
              stage='init',
              label_channels=label_channels)
          (*_, bbox_gt_list_init, candidate_list_init, bbox_weights_list_init,
           num_total_pos_init, num_total_neg_init) = cls_reg_targets_init
          num_total_samples_init = (
              num_total_pos_init +
              num_total_neg_init if self.sampling else num_total_pos_init)
  
          # target for refinement stage
          center_list, valid_flag_list = self.get_points(featmap_sizes,
                                                         img_metas, device)
          pts_coordinate_preds_refine = self.offset_to_pts(
              center_list, pts_preds_refine)
          bbox_list = []
          for i_img, center in enumerate(center_list):
              bbox = []
              for i_lvl in range(len(pts_preds_refine)):
                  bbox_preds_init = self.points2bbox(
                      pts_preds_init[i_lvl].detach())
                  bbox_shift = bbox_preds_init * self.point_strides[i_lvl]
                  bbox_center = torch.cat(
                      [center[i_lvl][:, :2], center[i_lvl][:, :2]], dim=1)
                  bbox.append(bbox_center +
                              bbox_shift[i_img].permute(1, 2, 0).reshape(-1, 4))
              bbox_list.append(bbox)
          cls_reg_targets_refine = self.get_targets(
              bbox_list,
              valid_flag_list,
              gt_bboxes,
              img_metas,
              gt_bboxes_ignore_list=gt_bboxes_ignore,
              gt_labels_list=gt_labels,
              stage='refine',
              label_channels=label_channels)
          (labels_list, label_weights_list, bbox_gt_list_refine,
           candidate_list_refine, bbox_weights_list_refine, num_total_pos_refine,
           num_total_neg_refine) = cls_reg_targets_refine
          num_total_samples_refine = (
              num_total_pos_refine +
              num_total_neg_refine if self.sampling else num_total_pos_refine)
  
          # compute loss
          losses_cls, losses_pts_init, losses_pts_refine = multi_apply(
              self.loss_single,
              cls_scores,
              pts_coordinate_preds_init,
              pts_coordinate_preds_refine,
              labels_list,
              label_weights_list,
              bbox_gt_list_init,
              bbox_weights_list_init,
              bbox_gt_list_refine,
              bbox_weights_list_refine,
              self.point_strides,
              num_total_samples_init=num_total_samples_init,
              num_total_samples_refine=num_total_samples_refine)
          loss_dict_all = {
              'loss_cls': losses_cls,
              'loss_pts_init': losses_pts_init,
              'loss_pts_refine': losses_pts_refine
          }
          return loss_dict_all
  
      # Same as base_dense_head/_get_bboxes_single except self._bbox_decode
      def _get_bboxes_single(self,
                             cls_score_list,
                             bbox_pred_list,
                             score_factor_list,
                             mlvl_priors,
                             img_meta,
                             cfg,
                             rescale=False,
                             with_nms=True,
                             **kwargs):
          """Transform outputs of a single image into bbox predictions.
  
          Args:
              cls_score_list (list[Tensor]): Box scores from all scale
                  levels of a single image, each item has shape
                  (num_priors * num_classes, H, W).
              bbox_pred_list (list[Tensor]): Box energies / deltas from
                  all scale levels of a single image, each item has shape
                  (num_priors * 4, H, W).
              score_factor_list (list[Tensor]): Score factor from all scale
                  levels of a single image. RepPoints head does not need
                  this value.
              mlvl_priors (list[Tensor]): Each element in the list is
                  the priors of a single level in feature pyramid, has shape
                  (num_priors, 2).
              img_meta (dict): Image meta info.
              cfg (mmcv.Config): Test / postprocessing configuration,
                  if None, test_cfg would be used.
              rescale (bool): If True, return boxes in original image space.
                  Default: False.
              with_nms (bool): If True, do nms before return boxes.
                  Default: True.
  
          Returns:
              tuple[Tensor]: Results of detected bboxes and labels. If with_nms
                  is False and mlvl_score_factor is None, return mlvl_bboxes and
                  mlvl_scores, else return mlvl_bboxes, mlvl_scores and
                  mlvl_score_factor. Usually with_nms is False is used for aug
                  test. If with_nms is True, then return the following format
  
                  - det_bboxes (Tensor): Predicted bboxes with shape \
                      [num_bboxes, 5], where the first 4 columns are bounding \
                      box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
                      column are scores between 0 and 1.
                  - det_labels (Tensor): Predicted labels of the corresponding \
                      box with shape [num_bboxes].
          """
          cfg = self.test_cfg if cfg is None else cfg
          assert len(cls_score_list) == len(bbox_pred_list)
          img_shape = img_meta['img_shape']
          nms_pre = cfg.get('nms_pre', -1)
  
          mlvl_bboxes = []
          mlvl_scores = []
          mlvl_labels = []
          for level_idx, (cls_score, bbox_pred, priors) in enumerate(
                  zip(cls_score_list, bbox_pred_list, mlvl_priors)):
              assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
              bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
  
              cls_score = cls_score.permute(1, 2,
                                            0).reshape(-1, self.cls_out_channels)
              if self.use_sigmoid_cls:
                  scores = cls_score.sigmoid()
              else:
                  scores = cls_score.softmax(-1)[:, :-1]
  
              # After https://github.com/open-mmlab/mmdetection/pull/6268/,
              # this operation keeps fewer bboxes under the same `nms_pre`.
              # There is no difference in performance for most models. If you
              # find a slight drop in performance, you can set a larger
              # `nms_pre` than before.
              results = filter_scores_and_topk(
                  scores, cfg.score_thr, nms_pre,
                  dict(bbox_pred=bbox_pred, priors=priors))
              scores, labels, _, filtered_results = results
  
              bbox_pred = filtered_results['bbox_pred']
              priors = filtered_results['priors']
  
              bboxes = self._bbox_decode(priors, bbox_pred,
                                         self.point_strides[level_idx],
                                         img_shape)
  
              mlvl_bboxes.append(bboxes)
              mlvl_scores.append(scores)
              mlvl_labels.append(labels)
  
          return self._bbox_post_process(
              mlvl_scores,
              mlvl_labels,
              mlvl_bboxes,
              img_meta['scale_factor'],
              cfg,
              rescale=rescale,
              with_nms=with_nms)
  
      def _bbox_decode(self, points, bbox_pred, stride, max_shape):
          bbox_pos_center = torch.cat([points[:, :2], points[:, :2]], dim=1)
          bboxes = bbox_pred * stride + bbox_pos_center
          x1 = bboxes[:, 0].clamp(min=0, max=max_shape[1])
          y1 = bboxes[:, 1].clamp(min=0, max=max_shape[0])
          x2 = bboxes[:, 2].clamp(min=0, max=max_shape[1])
          y2 = bboxes[:, 3].clamp(min=0, max=max_shape[0])
          decoded_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
          return decoded_bboxes
  
apis: numpy.tile torch.stack torch.std numpy.stack torch.zeros_like torch.linspace torch.tensor numpy.repeat torch.exp torch.zeros numpy.arange torch.nn.ModuleList torch.nn.Conv2d numpy.sqrt torch.nn.ReLU torch.cat torch.Tensor