text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from mmcv.runner import auto_fp16, force_fp32
  from torch.nn.modules.utils import _pair
  
  from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
  from mmdet.models.builder import HEADS, build_loss
  from mmdet.models.losses import accuracy
  
  
  @HEADS.register_module()
  class BBoxHead(nn.Module):
      """Simplest RoI head, with only two fc layers for classification and
      regression respectively."""
  
      def __init__(self,
                   with_avg_pool=False,
                   with_cls=True,
                   with_reg=True,
                   roi_feat_size=7,
                   in_channels=256,
                   num_classes=80,
                   bbox_coder=dict(
                       type='DeltaXYWHBBoxCoder',
                       clip_border=True,
                       target_means=[0., 0., 0., 0.],
                       target_stds=[0.1, 0.1, 0.2, 0.2]),
                   reg_class_agnostic=False,
                   reg_decoded_bbox=False,
                   loss_cls=dict(
                       type='CrossEntropyLoss',
                       use_sigmoid=False,
                       loss_weight=1.0),
                   loss_bbox=dict(
                       type='SmoothL1Loss', beta=1.0, loss_weight=1.0)):
          super(BBoxHead, self).__init__()
          assert with_cls or with_reg
          self.with_avg_pool = with_avg_pool
          self.with_cls = with_cls
          self.with_reg = with_reg
          self.roi_feat_size = _pair(roi_feat_size)
          self.roi_feat_area = self.roi_feat_size[0] * self.roi_feat_size[1]
          self.in_channels = in_channels
          self.num_classes = num_classes
          self.reg_class_agnostic = reg_class_agnostic
          self.reg_decoded_bbox = reg_decoded_bbox
          self.fp16_enabled = False
  
          self.bbox_coder = build_bbox_coder(bbox_coder)
          self.loss_cls = build_loss(loss_cls)
          self.loss_bbox = build_loss(loss_bbox)
  
          in_channels = self.in_channels
          if self.with_avg_pool:
              self.avg_pool = nn.AvgPool2d(self.roi_feat_size)
          else:
              in_channels *= self.roi_feat_area
          if self.with_cls:
              # need to add background class
              self.fc_cls = nn.Linear(in_channels, num_classes + 1)
          if self.with_reg:
              out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes
              self.fc_reg = nn.Linear(in_channels, out_dim_reg)
          self.debug_imgs = None
  
      def init_weights(self):
          # conv layers are already initialized by ConvModule
          if self.with_cls:
              nn.init.normal_(self.fc_cls.weight, 0, 0.01)
              nn.init.constant_(self.fc_cls.bias, 0)
          if self.with_reg:
              nn.init.normal_(self.fc_reg.weight, 0, 0.001)
              nn.init.constant_(self.fc_reg.bias, 0)
  
      @auto_fp16()
      def forward(self, x):
          if self.with_avg_pool:
              x = self.avg_pool(x)
          x = x.view(x.size(0), -1)
          cls_score = self.fc_cls(x) if self.with_cls else None
          bbox_pred = self.fc_reg(x) if self.with_reg else None
          return cls_score, bbox_pred
  
      def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes,
                             pos_gt_labels, cfg):
          """Calculate the ground truth for proposals in the single image
          according to the sampling results.
  
          Args:
              pos_bboxes (Tensor): Contains all the positive boxes,
                  has shape (num_pos, 4), the last dimension 4
                  represents [tl_x, tl_y, br_x, br_y].
              neg_bboxes (Tensor): Contains all the negative boxes,
                  has shape (num_neg, 4), the last dimension 4
                  represents [tl_x, tl_y, br_x, br_y].
              pos_gt_bboxes (Tensor): Contains all the gt_boxes,
                  has shape (num_gt, 4), the last dimension 4
                  represents [tl_x, tl_y, br_x, br_y].
              pos_gt_labels (Tensor): Contains all the gt_labels,
                  has shape (num_gt).
              cfg (obj:`ConfigDict`): `train_cfg` of R-CNN.
  
          Returns:
              Tuple[Tensor]: Ground truth for proposals
              in a single image. Containing the following Tensors:
  
                  - labels(Tensor): Gt_labels for all proposals, has
                    shape (num_proposals,).
                  - label_weights(Tensor): Labels_weights for all
                    proposals, has shape (num_proposals,).
                  - bbox_targets(Tensor):Regression target for all
                    proposals, has shape (num_proposals, 4), the
                    last dimension 4 represents [tl_x, tl_y, br_x, br_y].
                  - bbox_weights(Tensor):Regression weights for all
                    proposals, has shape (num_proposals, 4).
          """
          num_pos = pos_bboxes.size(0)
          num_neg = neg_bboxes.size(0)
          num_samples = num_pos + num_neg
  
          # original implementation uses new_zeros since BG are set to be 0
          # now use empty & fill because BG cat_id = num_classes,
          # FG cat_id = [0, num_classes-1]
          labels = pos_bboxes.new_full((num_samples,),
                                       self.num_classes,
                                       dtype=torch.long)
          label_weights = pos_bboxes.new_zeros(num_samples)
          bbox_targets = pos_bboxes.new_zeros(num_samples, 4)
          bbox_weights = pos_bboxes.new_zeros(num_samples, 4)
          if num_pos > 0:
              labels[:num_pos] = pos_gt_labels
              pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight
              label_weights[:num_pos] = pos_weight
              if not self.reg_decoded_bbox:
                  pos_bbox_targets = self.bbox_coder.encode(
                      pos_bboxes, pos_gt_bboxes)
              else:
                  # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
                  # is applied directly on the decoded bounding boxes, both
                  # the predicted boxes and regression targets should be with
                  # absolute coordinate format.
                  pos_bbox_targets = pos_gt_bboxes
              bbox_targets[:num_pos, :] = pos_bbox_targets
              bbox_weights[:num_pos, :] = 1
          if num_neg > 0:
              label_weights[-num_neg:] = 1.0
  
          return labels, label_weights, bbox_targets, bbox_weights
  
      def get_targets(self,
                      sampling_results,
                      gt_bboxes,
                      gt_labels,
                      rcnn_train_cfg,
                      concat=True):
          """Calculate the ground truth for all samples in a batch according to
          the sampling_results.
  
          Almost the same as the implementation in bbox_head, we passed
          additional parameters pos_inds_list and neg_inds_list to
          `_get_target_single` function.
  
          Args:
              sampling_results (List[obj:SamplingResults]): Assign results of
                  all images in a batch after sampling.
              gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch,
                  each tensor has shape (num_gt, 4),  the last dimension 4
                  represents [tl_x, tl_y, br_x, br_y].
              gt_labels (list[Tensor]): Gt_labels of all images in a batch,
                  each tensor has shape (num_gt,).
              rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
              concat (bool): Whether to concatenate the results of all
                  the images in a single batch.
  
          Returns:
              Tuple[Tensor]: Ground truth for proposals in a single image.
              Containing the following list of Tensors:
  
                  - labels (list[Tensor],Tensor): Gt_labels for all
                    proposals in a batch, each tensor in list has
                    shape (num_proposals,) when `concat=False`, otherwise
                    just a single tensor has shape (num_all_proposals,).
                  - label_weights (list[Tensor]): Labels_weights for
                    all proposals in a batch, each tensor in list has
                    shape (num_proposals,) when `concat=False`, otherwise
                    just a single tensor has shape (num_all_proposals,).
                  - bbox_targets (list[Tensor],Tensor): Regression target
                    for all proposals in a batch, each tensor in list
                    has shape (num_proposals, 4) when `concat=False`,
                    otherwise just a single tensor has shape
                    (num_all_proposals, 4), the last dimension 4 represents
                    [tl_x, tl_y, br_x, br_y].
                  - bbox_weights (list[tensor],Tensor): Regression weights for
                    all proposals in a batch, each tensor in list has shape
                    (num_proposals, 4) when `concat=False`, otherwise just a
                    single tensor has shape (num_all_proposals, 4).
          """
          pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
          neg_bboxes_list = [res.neg_bboxes for res in sampling_results]
          pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
          pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]
          labels, label_weights, bbox_targets, bbox_weights = multi_apply(
              self._get_target_single,
              pos_bboxes_list,
              neg_bboxes_list,
              pos_gt_bboxes_list,
              pos_gt_labels_list,
              cfg=rcnn_train_cfg)
  
          if concat:
              labels = torch.cat(labels, 0)
              label_weights = torch.cat(label_weights, 0)
              bbox_targets = torch.cat(bbox_targets, 0)
              bbox_weights = torch.cat(bbox_weights, 0)
          return labels, label_weights, bbox_targets, bbox_weights
  
      @force_fp32(apply_to=('cls_score', 'bbox_pred'))
      def loss(self,
               cls_score,
               bbox_pred,
               rois,
               labels,
               label_weights,
               bbox_targets,
               bbox_weights,
               reduction_override=None):
          losses = dict()
          if cls_score is not None:
              avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
              if cls_score.numel() > 0:
                  losses['loss_cls'] = self.loss_cls(cls_score, labels, label_weights,
                                                     avg_factor=avg_factor,
                                                     reduction_override=reduction_override)
                  losses['acc'] = accuracy(cls_score, labels)
          if bbox_pred is not None:
              bg_class_ind = self.num_classes
              # 0~self.num_classes-1 are FG, self.num_classes is BG
              pos_inds = (labels >= 0) & (labels < bg_class_ind)
              # do not perform bounding box regression for BG anymore.
              if pos_inds.any():
                  if self.reg_decoded_bbox:
                      # When the regression loss (e.g. `IouLoss`,
                      # `GIouLoss`, `DIouLoss`) is applied directly on
                      # the decoded bounding boxes, it decodes the
                      # already encoded coordinates to absolute format.
                      bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred)
                  if self.reg_class_agnostic:
                      pos_bbox_pred = bbox_pred.view(
                          bbox_pred.size(0), 4)[pos_inds.type(torch.bool)]
                  else:
                      pos_bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)[
                          pos_inds.type(torch.bool), labels[pos_inds.type(torch.bool)]]
                  losses['loss_bbox'] = self.loss_bbox(
                      pos_bbox_pred,
                      bbox_targets[pos_inds.type(torch.bool)],
                      bbox_weights[pos_inds.type(torch.bool)],
                      avg_factor=bbox_targets.size(0),
                      reduction_override=reduction_override)
              else:
                  losses['loss_bbox'] = bbox_pred[pos_inds].sum()
          return losses
  
      @force_fp32(apply_to=('cls_score', 'bbox_pred'))
      def get_bboxes(self,
                     rois,
                     cls_score,
                     bbox_pred,
                     img_shape,
                     scale_factor,
                     rescale=False,
                     cfg=None):
          """Transform network output for a batch into bbox predictions.
  
          If the input rois has batch dimension, the function would be in
          `batch_mode` and return is a tuple[list[Tensor], list[Tensor]],
          otherwise, the return is a tuple[Tensor, Tensor].
  
          Args:
              rois (Tensor): Boxes to be transformed. Has shape (num_boxes, 5)
                 or (B, num_boxes, 5)
              cls_score (list[Tensor] or Tensor): Box scores for
                 each scale level, each is a 4D-tensor, the channel number is
                 num_points * num_classes.
              bbox_pred (Tensor, optional): Box energies / deltas for each scale
                  level, each is a 4D-tensor, the channel number is
                  num_classes * 4.
              img_shape (Sequence[int] or torch.Tensor or Sequence[
                  Sequence[int]], optional): Maximum bounds for boxes, specifies
                  (H, W, C) or (H, W). If rois shape is (B, num_boxes, 4), then
                  the max_shape should be a Sequence[Sequence[int]]
                  and the length of max_shape should also be B.
              scale_factor (tuple[ndarray] or ndarray): Scale factor of the
                 image arange as (w_scale, h_scale, w_scale, h_scale). In
                 `batch_mode`, the scale_factor shape is tuple[ndarray].
              rescale (bool): If True, return boxes in original image space.
                  Default: False.
              cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None
  
          Returns:
              tuple[list[Tensor], list[Tensor]] or tuple[Tensor, Tensor]:
                  If the input has a batch dimension, the return value is
                  a tuple of the list. The first list contains the boxes of
                  the corresponding image in a batch, each tensor has the
                  shape (num_boxes, 5) and last dimension 5 represent
                  (tl_x, tl_y, br_x, br_y, score). Each Tensor in the second
                  list is the labels with shape (num_boxes, ). The length of
                  both lists should be equal to batch_size. Otherwise return
                  value is a tuple of two tensors, the first tensor is the
                  boxes with scores, the second tensor is the labels, both
                  have the same shape as the first case.
          """
          if isinstance(cls_score, list):
              cls_score = sum(cls_score) / float(len(cls_score))
  
          scores = F.softmax(
              cls_score, dim=-1) if cls_score is not None else None
  
          batch_mode = True
          if rois.ndim == 2:
              # e.g. AugTest, Cascade R-CNN, HTC, SCNet...
              batch_mode = False
  
              # add batch dimension
              if scores is not None:
                  scores = scores.unsqueeze(0)
              if bbox_pred is not None:
                  bbox_pred = bbox_pred.unsqueeze(0)
              rois = rois.unsqueeze(0)
  
          if bbox_pred is not None:
              bboxes = self.bbox_coder.decode(
                  rois[..., 1:], bbox_pred, max_shape=img_shape)
          else:
              bboxes = rois[..., 1:].clone()
              if img_shape is not None:
                  max_shape = bboxes.new_tensor(img_shape)[..., :2]
                  min_xy = bboxes.new_tensor(0)
                  max_xy = torch.cat(
                      [max_shape] * 2, dim=-1).flip(-1).unsqueeze(-2)
                  bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
                  bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
  
          if rescale and bboxes.size(-2) > 0:
              if not isinstance(scale_factor, tuple):
                  scale_factor = tuple([scale_factor])
              # B, 1, bboxes.size(-1)
              scale_factor = bboxes.new_tensor(scale_factor).unsqueeze(1).repeat(
                  1, 1,
                  bboxes.size(-1) // 4)
              bboxes /= scale_factor
  
          det_bboxes = []
          det_labels = []
          for (bbox, score) in zip(bboxes, scores):
              if cfg is not None:
                  det_bbox, det_label = multiclass_nms(bbox, score,
                                                       cfg.score_thr, cfg.nms,
                                                       cfg.max_per_img)
              else:
                  det_bbox, det_label = bbox, score
              det_bboxes.append(det_bbox)
              det_labels.append(det_label)
  
          if not batch_mode:
              det_bboxes = det_bboxes[0]
              det_labels = det_labels[0]
          return det_bboxes, det_labels
  
      @force_fp32(apply_to=('bbox_preds',))
      def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
          """Refine bboxes during training.
  
          Args:
              rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
                  and bs is the sampled RoIs per image. The first column is
                  the image id and the next 4 columns are x1, y1, x2, y2.
              labels (Tensor): Shape (n*bs, ).
              bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class).
              pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
                  is a gt bbox.
              img_metas (list[dict]): Meta info of each image.
  
          Returns:
              list[Tensor]: Refined bboxes of each image in a mini-batch.
  
          Example:
              >>> # xdoctest: +REQUIRES(module:kwarray)
              >>> import kwarray
              >>> import numpy as np
              >>> from mmdet.core.bbox.demodata import random_boxes
              >>> self = BBoxHead(reg_class_agnostic=True)
              >>> n_roi = 2
              >>> n_img = 4
              >>> scale = 512
              >>> rng = np.random.RandomState(0)
              >>> img_metas = [{'img_shape': (scale, scale)}
              ...              for _ in range(n_img)]
              >>> # Create rois in the expected format
              >>> roi_boxes = random_boxes(n_roi, scale=scale, rng=rng)
              >>> img_ids = torch.randint(0, n_img, (n_roi,))
              >>> img_ids = img_ids.float()
              >>> rois = torch.cat([img_ids[:, None], roi_boxes], dim=1)
              >>> # Create other args
              >>> labels = torch.randint(0, 2, (n_roi,)).long()
              >>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
              >>> # For each image, pretend random positive boxes are gts
              >>> is_label_pos = (labels.numpy() > 0).astype(np.int)
              >>> lbl_per_img = kwarray.group_items(is_label_pos,
              ...                                   img_ids.numpy())
              >>> pos_per_img = [sum(lbl_per_img.get(gid, []))
              ...                for gid in range(n_img)]
              >>> pos_is_gts = [
              >>>     torch.randint(0, 2, (npos,)).byte().sort(
              >>>         descending=True)[0]
              >>>     for npos in pos_per_img
              >>> ]
              >>> bboxes_list = self.refine_bboxes(rois, labels, bbox_preds,
              >>>                    pos_is_gts, img_metas)
              >>> print(bboxes_list)
          """
          img_ids = rois[:, 0].long().unique(sorted=True)
          assert img_ids.numel() <= len(img_metas)
  
          bboxes_list = []
          for i in range(len(img_metas)):
              inds = torch.nonzero(
                  rois[:, 0] == i, as_tuple=False).squeeze(dim=1)
              num_rois = inds.numel()
  
              bboxes_ = rois[inds, 1:]
              label_ = labels[inds]
              bbox_pred_ = bbox_preds[inds]
              img_meta_ = img_metas[i]
              pos_is_gts_ = pos_is_gts[i]
  
              bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
                                             img_meta_)
  
              # filter gt bboxes
              pos_keep = 1 - pos_is_gts_
              keep_inds = pos_is_gts_.new_ones(num_rois)
              keep_inds[:len(pos_is_gts_)] = pos_keep
  
              bboxes_list.append(bboxes[keep_inds.type(torch.bool)])
  
          return bboxes_list
  
      @force_fp32(apply_to=('bbox_pred',))
      def regress_by_class(self, rois, label, bbox_pred, img_meta):
          """Regress the bbox for the predicted class. Used in Cascade R-CNN.
  
          Args:
              rois (Tensor): shape (n, 4) or (n, 5)
              label (Tensor): shape (n, )
              bbox_pred (Tensor): shape (n, 4*(#class)) or (n, 4)
              img_meta (dict): Image meta info.
  
          Returns:
              Tensor: Regressed bboxes, the same shape as input rois.
          """
          assert rois.size(1) == 4 or rois.size(1) == 5, repr(rois.shape)
  
          if not self.reg_class_agnostic:
              label = label * 4
              inds = torch.stack((label, label + 1, label + 2, label + 3), 1)
              bbox_pred = torch.gather(bbox_pred, 1, inds)
          assert bbox_pred.size(1) == 4
  
          if rois.size(1) == 4:
              new_rois = self.bbox_coder.decode(
                  rois, bbox_pred, max_shape=img_meta['img_shape'])
          else:
              bboxes = self.bbox_coder.decode(
                  rois[:, 1:], bbox_pred, max_shape=img_meta['img_shape'])
              new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
  
          return new_rois
  