text: import numpy as np
  import torch
  from mmcv.cnn import bias_init_with_prob, normal_init
  from mmcv.runner import force_fp32
  from torch import nn as nn
  
  from mmdet3d.core import (PseudoSampler, box3d_multiclass_nms, limit_period,
                            xywhr2xyxyr)
  from mmdet.core import (build_anchor_generator, build_assigner,
                          build_bbox_coder, build_sampler, multi_apply)
  from mmdet.models import HEADS
  from ..builder import build_loss
  from .train_mixins import AnchorTrainMixin
  
  
  @HEADS.register_module()
  class FVNetAnchorHead(nn.Module, AnchorTrainMixin):
  
      def __init__(self,
                   num_classes,
                   in_channels,
                   train_cfg,
                   test_cfg,
                   feat_channels=256,
                   use_direction_classifier=True,
                  anchor_generator=dict(
                      type='LineAnchorGenerator',
                      dist_list=list(range(0, 80, 4)),
                      num_bins=4,
                      ranges=[[0, -40.0, -1.78, 70.4, 40.0, -1.78]],
                      sizes=[[1.6, 3.9, 1.56]],
                      rotations=[0, 1.57],
                      reshape_out=True,
                      scales=[1, 1, 1, 1, 1]),
                   assigner_per_size=False,
                   assign_per_class=False,
                   diff_rad_by_sin=True,
                   dir_offset=0,
                   dir_limit_offset=1,
                   bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
                   loss_cls=dict(
                       type='CrossEntropyLoss',
                       use_sigmoid=True,
                       loss_weight=1.0),
                   loss_bbox=dict(
                       type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
                   loss_dir=dict(type='CrossEntropyLoss', loss_weight=0.2)):
          super().__init__()
          self.in_channels = in_channels
          self.num_classes = num_classes
          self.feat_channels = feat_channels
          self.diff_rad_by_sin = diff_rad_by_sin
          self.use_direction_classifier = use_direction_classifier
          self.train_cfg = train_cfg
          self.test_cfg = test_cfg
          self.assigner_per_size = assigner_per_size
          self.assign_per_class = assign_per_class
          self.dir_offset = dir_offset
          self.dir_limit_offset = dir_limit_offset
          self.fp16_enabled = False
  
          # build anchor generator
          self.anchor_generator = build_anchor_generator(anchor_generator)
          # In 3D detection, the anchor stride is connected with anchor size
          self.num_anchors = self.anchor_generator.num_base_anchors
          # build box coder
          self.bbox_coder = build_bbox_coder(bbox_coder)
          self.box_code_size = self.bbox_coder.code_size
  
          # build loss function
          self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
          self.sampling = loss_cls['type'] not in ['FocalLoss', 'GHMC']
          if not self.use_sigmoid_cls:
              self.num_classes += 1
          self.loss_cls = build_loss(loss_cls)
          self.loss_bbox = build_loss(loss_bbox)
          self.loss_dir = build_loss(loss_dir)
          self.fp16_enabled = False
  
          self._init_layers()
          self._init_assigner_sampler()
  
      def _init_assigner_sampler(self):
          """Initialize the target assigner and sampler of the head."""
          if self.train_cfg is None:
              return
  
          if self.sampling:
              self.bbox_sampler = build_sampler(self.train_cfg.sampler)
          else:
              self.bbox_sampler = PseudoSampler()
          if isinstance(self.train_cfg.assigner, dict):
              self.bbox_assigner = build_assigner(self.train_cfg.assigner)
          elif isinstance(self.train_cfg.assigner, list):
              self.bbox_assigner = [
                  build_assigner(res) for res in self.train_cfg.assigner
              ]
  
      def _init_layers(self):
          """Initialize neural network layers of the head."""
          self.cls_out_channels = self.num_anchors * self.num_classes\
              * self.anchor_generator.num_bins
          self.reg_out_channels = self.num_anchors * self.box_code_size\
              * self.anchor_generator.num_bins
          self.dir_cls_out_channels = self.num_anchors * 2\
              * self.anchor_generator.num_bins
          self.conv_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1)
          self.conv_reg = nn.Conv2d(self.feat_channels, self.reg_out_channels, 1)
          if self.use_direction_classifier:
              self.conv_dir_cls = nn.Conv2d(self.feat_channels,
                                            self.dir_cls_out_channels, 1)
  
      def init_weights(self):
          """Initialize the weights of head."""
          bias_cls = bias_init_with_prob(0.01)
          normal_init(self.conv_cls, std=0.01, bias=bias_cls)
          normal_init(self.conv_reg, std=0.01)
  
      def forward_single(self, x):
          """Forward function on a single-scale feature map.
  
          Args:
              x (torch.Tensor): Input features.
  
          Returns:
              tuple[torch.Tensor]: Contain score of each class, bbox \
                  regression and direction classification predictions.
          """
          cls_score = self.conv_cls(x)
          bbox_pred = self.conv_reg(x)
          dir_cls_preds = None
          if self.use_direction_classifier:
              dir_cls_preds = self.conv_dir_cls(x)
          return cls_score, bbox_pred, dir_cls_preds
  
      def forward(self, feats):
          """Forward pass.
  
          Args:
              feats (list[torch.Tensor]): Multi-level features, e.g.,
                  features produced by FPN.
  
          Returns:
              tuple[list[torch.Tensor]]: Multi-level class score, bbox \
                  and direction predictions.
          """
          return multi_apply(self.forward_single, feats)
  
      def get_anchors(self, featmap_sizes, input_metas, device='cuda'):
          """Get anchors according to feature map sizes.
  
          Args:
              featmap_sizes (list[tuple]): Multi-level feature map sizes.
              input_metas (list[dict]): contain pcd and img's meta info.
              device (str): device of current module.
  
          Returns:
              list[list[torch.Tensor]]: Anchors of each image, valid flags \
                  of each image.
          """
          num_imgs = len(input_metas)
          # since feature map sizes of all images are the same, we only compute
          # anchors for one time
          multi_level_anchors, val_masks = self.anchor_generator.grid_anchors(
              featmap_sizes, device=device)
          multi_level_anchors = [res.to(device) for res in multi_level_anchors]
          anchor_list = [multi_level_anchors for _ in range(num_imgs)]
          val_masks_list = [val_masks for _ in range(num_imgs)]
          return anchor_list, val_masks_list
      
      def loss_single(self, val_mask, cls_score, bbox_pred, dir_cls_preds, labels,
                      label_weights, bbox_targets, bbox_weights, dir_targets,
                      dir_weights, num_total_samples):
          """Calculate loss of Single-level results.
  
          Args:
              cls_score (torch.Tensor): Class score in single-level.
              bbox_pred (torch.Tensor): Bbox prediction in single-level.
              dir_cls_preds (torch.Tensor): Predictions of direction class
                  in single-level.
              labels (torch.Tensor): Labels of class.
              label_weights (torch.Tensor): Weights of class loss.
              bbox_targets (torch.Tensor): Targets of bbox predictions.
              bbox_weights (torch.Tensor): Weights of bbox loss.
              dir_targets (torch.Tensor): Targets of direction predictions.
              dir_weights (torch.Tensor): Weights of direction loss.
              num_total_samples (int): The number of valid samples.
  
          Returns:
              tuple[torch.Tensor]: Losses of class, bbox \
                  and direction, respectively.
          """
          # classification loss
          val_mask = [val_mask] * cls_score.shape[0]
          val_mask = torch.cat(val_mask, dim=0)
          val_mask = val_mask.reshape(*val_mask.shape[:3], -1)
          if num_total_samples is None:
              num_total_samples = int(cls_score.shape[0])
          labels = labels.reshape(-1)
          label_weights = label_weights.reshape(-1)
          cls_score = cls_score.permute(0, 2, 3, 1).reshape(-1, self.num_classes)
          val_mask = val_mask.reshape(-1, self.num_classes)
          cls_score = cls_score[val_mask].reshape(-1, self.num_classes)
          assert labels.max().item() <= self.num_classes
          loss_cls = self.loss_cls(
              cls_score, labels, label_weights, avg_factor=num_total_samples)
  
          # regression loss
          bbox_pred = bbox_pred.permute(0, 2, 3,
                                        1).reshape(-1, self.box_code_size)
          bbox_pred = bbox_pred[val_mask[:, 0]]
          bbox_targets = bbox_targets.reshape(-1, self.box_code_size)
          bbox_weights = bbox_weights.reshape(-1, self.box_code_size)
  
          bg_class_ind = self.num_classes
          pos_inds = ((labels >= 0)
                      & (labels < bg_class_ind)).nonzero().reshape(-1)
          num_pos = len(pos_inds)
  
          pos_bbox_pred = bbox_pred[pos_inds]
          pos_bbox_targets = bbox_targets[pos_inds]
          pos_bbox_weights = bbox_weights[pos_inds]
  
          # dir loss
          if self.use_direction_classifier:
              dir_cls_preds = dir_cls_preds.permute(0, 2, 3, 1).reshape(-1, 2)
              dir_cls_preds = dir_cls_preds[val_mask[:, 0]]
              dir_targets = dir_targets.reshape(-1)
              dir_weights = dir_weights.reshape(-1)
              pos_dir_cls_preds = dir_cls_preds[pos_inds]
              pos_dir_targets = dir_targets[pos_inds]
              pos_dir_weights = dir_weights[pos_inds]
  
          if num_pos > 0:
              code_weight = self.train_cfg.get('code_weight', None)
              if code_weight:
                  pos_bbox_weights = pos_bbox_weights * bbox_weights.new_tensor(
                      code_weight)
              if self.diff_rad_by_sin:
                  pos_bbox_pred, pos_bbox_targets = self.add_sin_difference(
                      pos_bbox_pred, pos_bbox_targets)
              loss_bbox = self.loss_bbox(
                  pos_bbox_pred,
                  pos_bbox_targets,
                  pos_bbox_weights,
                  avg_factor=num_total_samples)
  
              # direction classification loss
              loss_dir = None
              if self.use_direction_classifier:
                  loss_dir = self.loss_dir(
                      pos_dir_cls_preds,
                      pos_dir_targets,
                      pos_dir_weights,
                      avg_factor=num_total_samples)
          else:
              loss_bbox = pos_bbox_pred.sum()
              if self.use_direction_classifier:
                  loss_dir = pos_dir_cls_preds.sum()
  
          return loss_cls, loss_bbox, loss_dir
  
      @staticmethod
      def add_sin_difference(boxes1, boxes2):
          """Convert the rotation difference to difference in sine function.
  
          Args:
              boxes1 (torch.Tensor): Original Boxes in shape (NxC), where C>=7
                  and the 7th dimension is rotation dimension.
              boxes2 (torch.Tensor): Target boxes in shape (NxC), where C>=7 and
                  the 7th dimension is rotation dimension.
  
          Returns:
              tuple[torch.Tensor]: ``boxes1`` and ``boxes2`` whose 7th \
                  dimensions are changed.
          """
          rad_pred_encoding = torch.sin(boxes1[..., 6:7]) * torch.cos(
              boxes2[..., 6:7])
          rad_tg_encoding = torch.cos(boxes1[..., 6:7]) * torch.sin(boxes2[...,
                                                                           6:7])
          boxes1 = torch.cat(
              [boxes1[..., :6], rad_pred_encoding, boxes1[..., 7:]], dim=-1)
          boxes2 = torch.cat([boxes2[..., :6], rad_tg_encoding, boxes2[..., 7:]],
                             dim=-1)
          return boxes1, boxes2
  
      @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'dir_cls_preds'))
      def loss(self,
               cls_scores,
               bbox_preds,
               dir_cls_preds,
               gt_bboxes,
               gt_labels,
               input_metas,
               gt_bboxes_ignore=None):
          """Calculate losses.
  
          Args:
              cls_scores (list[torch.Tensor]): Multi-level class scores.
              bbox_preds (list[torch.Tensor]): Multi-level bbox predictions.
              dir_cls_preds (list[torch.Tensor]): Multi-level direction
                  class predictions.
              gt_bboxes (list[:obj:`BaseInstance3DBoxes`]): Gt bboxes
                  of each sample.
              gt_labels (list[torch.Tensor]): Gt labels of each sample.
              input_metas (list[dict]): Contain pcd and img's meta info.
              gt_bboxes_ignore (None | list[torch.Tensor]): Specify
                  which bounding.
  
          Returns:
              dict[str, list[torch.Tensor]]: Classification, bbox, and \
                  direction losses of each level.
  
                  - loss_cls (list[torch.Tensor]): Classification losses.
                  - loss_bbox (list[torch.Tensor]): Box regression losses.
                  - loss_dir (list[torch.Tensor]): Direction classification \
                      losses.
          """
          featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
          assert len(featmap_sizes) == self.anchor_generator.num_levels
          device = cls_scores[0].device
          anchor_list, val_mask_list = self.get_anchors(
              featmap_sizes, input_metas, device=device)
  
          # # Plot anchor centers
          # import matplotlib.pyplot as plt
          # for anchor, mask in zip(anchor_list[0], val_mask_list[0]):
          #     anchor = anchor.cpu()
          #     mask = mask.cpu()
          #     anchor = anchor[mask.reshape(-1)]
          #     plt.scatter(anchor[..., 1], anchor[..., 0], s=0.01)
          # plt.savefig('test1.png')
          # plt.cla()
          # for anchor, mask in zip(anchor_list[0], val_mask_list[0]):
          #     anchor = anchor.cpu()
          #     mask = mask.cpu()
          #     anchor = anchor[mask.reshape(-1)]
          #     plt.scatter(anchor[..., 0], anchor[..., 2], s=0.1)
          # plt.ylim(-2, -1)
          # plt.savefig('test2.png')
          # plt.cla()
          # for anchor, mask in zip(anchor_list[0], val_mask_list[0]):
          #     anchor = anchor.cpu()
          #     mask = mask.cpu()
          #     anchor = anchor[mask.reshape(-1)]
          #     plt.scatter(anchor[..., 1], anchor[..., 2], s=0.1)
          # plt.savefig('test3.png')
          # plt.cla()
  
          anchor_list = [[anchor[mask.reshape(-1)] for anchor, mask\
              in zip(anchor_list[i], val_mask_list[i])]\
              for i in range(len(anchor_list))]
          label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
          cls_reg_targets = self.anchor_target_3d(
              anchor_list,
              gt_bboxes,
              input_metas,
              gt_bboxes_ignore_list=gt_bboxes_ignore,
              gt_labels_list=gt_labels,
              num_classes=self.num_classes,
              label_channels=label_channels,
              sampling=self.sampling)
  
          if cls_reg_targets is None:
              return None
          (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
           dir_targets_list, dir_weights_list, num_total_pos,
           num_total_neg) = cls_reg_targets
          num_total_samples = (
              num_total_pos + num_total_neg if self.sampling else num_total_pos)
  
          # num_total_samples = None
          losses_cls, losses_bbox, losses_dir = multi_apply(
              self.loss_single,
              val_mask_list[0],
              cls_scores,
              bbox_preds,
              dir_cls_preds,
              labels_list,
              label_weights_list,
              bbox_targets_list,
              bbox_weights_list,
              dir_targets_list,
              dir_weights_list,
              num_total_samples=num_total_samples)
          return dict(
              loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dir=losses_dir)
  
      def get_bboxes(self,
                     cls_scores,
                     bbox_preds,
                     dir_cls_preds,
                     input_metas,
                     cfg=None,
                     rescale=False):
          """Get bboxes of anchor head.
  
          Args:
              cls_scores (list[torch.Tensor]): Multi-level class scores.
              bbox_preds (list[torch.Tensor]): Multi-level bbox predictions.
              dir_cls_preds (list[torch.Tensor]): Multi-level direction
                  class predictions.
              input_metas (list[dict]): Contain pcd and img's meta info.
              cfg (None | :obj:`ConfigDict`): Training or testing config.
              rescale (list[torch.Tensor]): Whether th rescale bbox.
  
          Returns:
              list[tuple]: Prediction resultes of batches.
          """
          assert len(cls_scores) == len(bbox_preds)
          assert len(cls_scores) == len(dir_cls_preds)
          num_levels = len(cls_scores)
          featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
          device = cls_scores[0].device
          mlvl_anchors, val_masks = self.get_anchors(featmap_sizes, input_metas,
                                                     device=device)
          mlvl_anchors, val_masks = mlvl_anchors[0], val_masks[0]
          mlvl_anchors = [res.to(device) for res in mlvl_anchors]
          # mlvl_anchors = self.anchor_generator.grid_anchors(
          #     featmap_sizes, device=device)
          mlvl_anchors = [
              anchor.reshape(-1, self.box_code_size) for anchor in mlvl_anchors
          ]
  
          result_list = []
          for img_id in range(len(input_metas)):
              cls_score_list = [
                  cls_scores[i][img_id].detach() for i in range(num_levels)
              ]
              bbox_pred_list = [
                  bbox_preds[i][img_id].detach() for i in range(num_levels)
              ]
              dir_cls_pred_list = [
                  dir_cls_preds[i][img_id].detach() for i in range(num_levels)
              ]
  
              input_meta = input_metas[img_id]
              proposals = self.get_bboxes_single(val_masks, cls_score_list, bbox_pred_list,
                                                 dir_cls_pred_list, mlvl_anchors,
                                                 input_meta, cfg, rescale)
              result_list.append(proposals)
          return result_list
  
      def get_bboxes_single(self,
                            val_masks,
                            cls_scores,
                            bbox_preds,
                            dir_cls_preds,
                            mlvl_anchors,
                            input_meta,
                            cfg=None,
                            rescale=False):
          """Get bboxes of single branch.
  
          Args:
              cls_scores (torch.Tensor): Class score in single batch.
              bbox_preds (torch.Tensor): Bbox prediction in single batch.
              dir_cls_preds (torch.Tensor): Predictions of direction class
                  in single batch.
              mlvl_anchors (List[torch.Tensor]): Multi-level anchors
                  in single batch.
              input_meta (list[dict]): Contain pcd and img's meta info.
              cfg (None | :obj:`ConfigDict`): Training or testing config.
              rescale (list[torch.Tensor]): whether th rescale bbox.
  
          Returns:
              tuple: Contain predictions of single batch.
  
                  - bboxes (:obj:`BaseInstance3DBoxes`): Predicted 3d bboxes.
                  - scores (torch.Tensor): Class score of each bbox.
                  - labels (torch.Tensor): Label of each bbox.
          """
          cfg = self.test_cfg if cfg is None else cfg
          assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
          mlvl_bboxes = []
          mlvl_scores = []
          mlvl_dir_scores = []
          for cls_score, bbox_pred, dir_cls_pred, anchors, val_mask in zip(
                  cls_scores, bbox_preds, dir_cls_preds, mlvl_anchors, val_masks):
              assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
              assert cls_score.size()[-2:] == dir_cls_pred.size()[-2:]
              val_mask = val_mask.reshape(-1)
              anchors = anchors[val_mask]
              dir_cls_pred = dir_cls_pred.permute(1, 2, 0).reshape(-1, 2)
              dir_cls_pred = dir_cls_pred[val_mask]
              dir_cls_score = torch.max(dir_cls_pred, dim=-1)[1]
  
              cls_score = cls_score.permute(1, 2,
                                            0).reshape(-1, self.num_classes)
              cls_score = cls_score[val_mask]
              if self.use_sigmoid_cls:
                  scores = cls_score.sigmoid()
              else:
                  scores = cls_score.softmax(-1)
              bbox_pred = bbox_pred.permute(1, 2,
                                            0).reshape(-1, self.box_code_size)
              bbox_pred = bbox_pred[val_mask]
  
              nms_pre = cfg.get('nms_pre', -1)
              if nms_pre > 0 and scores.shape[0] > nms_pre:
                  if self.use_sigmoid_cls:
                      max_scores, _ = scores.max(dim=1)
                  else:
                      max_scores, _ = scores[:, :-1].max(dim=1)
                  _, topk_inds = max_scores.topk(nms_pre)
                  anchors = anchors[topk_inds, :]
                  bbox_pred = bbox_pred[topk_inds, :]
                  scores = scores[topk_inds, :]
                  dir_cls_score = dir_cls_score[topk_inds]
  
              bboxes = self.bbox_coder.decode(anchors, bbox_pred)
              mlvl_bboxes.append(bboxes)
              mlvl_scores.append(scores)
              mlvl_dir_scores.append(dir_cls_score)
  
          mlvl_bboxes = torch.cat(mlvl_bboxes)
          mlvl_bboxes_for_nms = xywhr2xyxyr(input_meta['box_type_3d'](
              mlvl_bboxes, box_dim=self.box_code_size).bev)
          mlvl_scores = torch.cat(mlvl_scores)
          mlvl_dir_scores = torch.cat(mlvl_dir_scores)
  
          if self.use_sigmoid_cls:
              # Add a dummy background class to the front when using sigmoid
              padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
              mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
  
          score_thr = cfg.get('score_thr', 0)
          results = box3d_multiclass_nms(mlvl_bboxes, mlvl_bboxes_for_nms,
                                         mlvl_scores, score_thr, cfg.max_num,
                                         cfg, mlvl_dir_scores)
          bboxes, scores, labels, dir_scores = results
          if bboxes.shape[0] > 0:
              dir_rot = limit_period(bboxes[..., 6] - self.dir_offset,
                                     self.dir_limit_offset, np.pi)
              bboxes[..., 6] = (
                  dir_rot + self.dir_offset +
                  np.pi * dir_scores.to(bboxes.dtype))
          bboxes = input_meta['box_type_3d'](bboxes, box_dim=self.box_code_size)
          return bboxes, scores, labels
  