text: import numpy as np
  import spconv
  import torch
  import torch.nn as nn
  
  from ...ops.roiaware_pool3d import roiaware_pool3d_utils
  from .roi_head_template import RoIHeadTemplate
  
  
  class PartA2FCHead(RoIHeadTemplate):
      def __init__(self, input_channels, model_cfg, num_class=1):
          super().__init__(num_class=num_class, model_cfg=model_cfg, nusc=False)
          self.model_cfg = model_cfg
          self.nusc = nusc
  
          self.SA_modules = nn.ModuleList()
          block = self.post_act_block
  
          c0 = self.model_cfg.ROI_AWARE_POOL.NUM_FEATURES // 2
          self.conv_part = spconv.SparseSequential(
              block(4, 64, 3, padding=1, indice_key='rcnn_subm1'),
              block(64, c0, 3, padding=1, indice_key='rcnn_subm1_1'),
          )
          self.conv_rpn = spconv.SparseSequential(
              block(input_channels, 64, 3, padding=1, indice_key='rcnn_subm2'),
              block(64, c0, 3, padding=1, indice_key='rcnn_subm1_2'),
          )
  
          shared_fc_list = []
          pool_size = self.model_cfg.ROI_AWARE_POOL.POOL_SIZE
          pre_channel = self.model_cfg.ROI_AWARE_POOL.NUM_FEATURES * pool_size * pool_size * pool_size
          for k in range(0, self.model_cfg.SHARED_FC.__len__()):
              shared_fc_list.extend([
                  nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False),
                  nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]),
                  nn.ReLU()
              ])
              pre_channel = self.model_cfg.SHARED_FC[k]
  
              if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
                  shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
  
          self.shared_fc_layer = nn.Sequential(*shared_fc_list)
  
          self.cls_layers = self.make_fc_layers(
              input_channels=pre_channel, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC
          )
          self.reg_layers = self.make_fc_layers(
              input_channels=pre_channel,
              output_channels=self.box_coder.code_size * self.num_class,
              fc_list=self.model_cfg.REG_FC
          )
  
          self.roiaware_pool3d_layer = roiaware_pool3d_utils.RoIAwarePool3d(
              out_size=self.model_cfg.ROI_AWARE_POOL.POOL_SIZE,
              max_pts_each_voxel=self.model_cfg.ROI_AWARE_POOL.MAX_POINTS_PER_VOXEL, nusc=self.nusc
          )
          self.init_weights(weight_init='xavier')
  
      def init_weights(self, weight_init='xavier'):
          if weight_init == 'kaiming':
              init_func = nn.init.kaiming_normal_
          elif weight_init == 'xavier':
              init_func = nn.init.xavier_normal_
          elif weight_init == 'normal':
              init_func = nn.init.normal_
          else:
              raise NotImplementedError
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
                  if weight_init == 'normal':
                      init_func(m.weight, mean=0, std=0.001)
                  else:
                      init_func(m.weight)
                  if m.bias is not None:
                      nn.init.constant_(m.bias, 0)
          nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001)
  
      def post_act_block(self, in_channels, out_channels, kernel_size, indice_key, stride=1, padding=0, conv_type='subm'):
          if conv_type == 'subm':
              m = spconv.SparseSequential(
                  spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key),
                  nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
                  nn.ReLU(),
              )
          elif conv_type == 'spconv':
              m = spconv.SparseSequential(
                  spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
                                      bias=False, indice_key=indice_key),
                  nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
                  nn.ReLU(),
              )
          elif conv_type == 'inverseconv':
              m = spconv.SparseSequential(
                  spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size,
                                             indice_key=indice_key, bias=False),
                  nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
                  nn.ReLU(),
              )
          else:
              raise NotImplementedError
          return m
  
      def roiaware_pool(self, batch_dict):
          """
          Args:
              batch_dict:
                  batch_size:
                  rois: (B, num_rois, 7 + C)
                  point_coords: (num_points, 4)  [bs_idx, x, y, z]
                  point_features: (num_points, C)
                  point_cls_scores: (N1 + N2 + N3 + ..., 1)
                  point_part_offset: (N1 + N2 + N3 + ..., 3)
          Returns:
  
          """
          batch_size = batch_dict['batch_size']
          batch_idx = batch_dict['point_coords'][:, 0]
          point_coords = batch_dict['point_coords'][:, 1:4]
          point_features = batch_dict['point_features']
          part_features = torch.cat((
              batch_dict['point_part_offset'] if not self.model_cfg.get('DISABLE_PART', False) else point_coords,
              batch_dict['point_cls_scores'].view(-1, 1).detach()
          ), dim=1)
          part_features[part_features[:, -1] < self.model_cfg.SEG_MASK_SCORE_THRESH, 0:3] = 0
  
          rois = batch_dict['rois']
  
          pooled_part_features_list, pooled_rpn_features_list = [], []
  
          for bs_idx in range(batch_size):
              bs_mask = (batch_idx == bs_idx)
              cur_point_coords = point_coords[bs_mask]
              cur_part_features = part_features[bs_mask]
              cur_rpn_features = point_features[bs_mask]
              cur_roi = rois[bs_idx][:, 0:7].contiguous()  # (N, 7)
  
              pooled_part_features = self.roiaware_pool3d_layer.forward(
                  cur_roi, cur_point_coords, cur_part_features, pool_method='avg'
              )  # (N, out_x, out_y, out_z, 4)
              pooled_rpn_features = self.roiaware_pool3d_layer.forward(
                  cur_roi, cur_point_coords, cur_rpn_features, pool_method='max'
              )  # (N, out_x, out_y, out_z, C)
  
              pooled_part_features_list.append(pooled_part_features)
              pooled_rpn_features_list.append(pooled_rpn_features)
  
          pooled_part_features = torch.cat(pooled_part_features_list, dim=0)  # (B * N, out_x, out_y, out_z, 4)
          pooled_rpn_features = torch.cat(pooled_rpn_features_list, dim=0)  # (B * N, out_x, out_y, out_z, C)
  
          return pooled_part_features, pooled_rpn_features
  
      @staticmethod
      def fake_sparse_idx(sparse_idx, batch_size_rcnn):
          print('Warning: Sparse_Idx_Shape(%s) \r' % (str(sparse_idx.shape)), end='', flush=True)
          # at most one sample is non-empty, then fake the first voxels of each sample(BN needs at least
          # two values each channel) as non-empty for the below calculation
          sparse_idx = sparse_idx.new_zeros((batch_size_rcnn, 3))
          bs_idxs = torch.arange(batch_size_rcnn).type_as(sparse_idx).view(-1, 1)
          sparse_idx = torch.cat((bs_idxs, sparse_idx), dim=1)
          return sparse_idx
  
      def forward(self, batch_dict):
          """
          Args:
              batch_dict:
  
          Returns:
  
          """
          targets_dict = self.proposal_layer(
              batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
          )
          if self.training:
              targets_dict = self.assign_targets(batch_dict)
              batch_dict['rois'] = targets_dict['rois']
              batch_dict['roi_labels'] = targets_dict['roi_labels']
  
          # RoI aware pooling
          pooled_part_features, pooled_rpn_features = self.roiaware_pool(batch_dict)
          batch_size_rcnn = pooled_part_features.shape[0]  # (B * N, out_x, out_y, out_z, 4)
  
          # transform to sparse tensors
          sparse_shape = np.array(pooled_part_features.shape[1:4], dtype=np.int32)
          sparse_idx = pooled_part_features.sum(dim=-1).nonzero()  # (non_empty_num, 4) ==> [bs_idx, x_idx, y_idx, z_idx]
          if sparse_idx.shape[0] < 3:
              sparse_idx = self.fake_sparse_idx(sparse_idx, batch_size_rcnn)
              if self.training:
                  # these are invalid samples
                  targets_dict['rcnn_cls_labels'].fill_(-1)
                  targets_dict['reg_valid_mask'].fill_(-1)
  
          part_features = pooled_part_features[sparse_idx[:, 0], sparse_idx[:, 1], sparse_idx[:, 2], sparse_idx[:, 3]]
          rpn_features = pooled_rpn_features[sparse_idx[:, 0], sparse_idx[:, 1], sparse_idx[:, 2], sparse_idx[:, 3]]
          coords = sparse_idx.int()
          part_features = spconv.SparseConvTensor(part_features, coords, sparse_shape, batch_size_rcnn)
          rpn_features = spconv.SparseConvTensor(rpn_features, coords, sparse_shape, batch_size_rcnn)
  
          # forward rcnn network
          x_part = self.conv_part(part_features)
          x_rpn = self.conv_rpn(rpn_features)
  
          merged_feature = torch.cat((x_rpn.features, x_part.features), dim=1)  # (N, C)
          shared_feature = spconv.SparseConvTensor(merged_feature, coords, sparse_shape, batch_size_rcnn)
          shared_feature = shared_feature.dense().view(batch_size_rcnn, -1, 1)
  
          shared_feature = self.shared_fc_layer(shared_feature)
  
          rcnn_cls = self.cls_layers(shared_feature).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, 1 or 2)
          rcnn_reg = self.reg_layers(shared_feature).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, C)
  
          if not self.training:
              batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
                  batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
              )
              batch_dict['batch_cls_preds'] = batch_cls_preds
              batch_dict['batch_box_preds'] = batch_box_preds
              batch_dict['cls_preds_normalized'] = False
          else:
              targets_dict['rcnn_cls'] = rcnn_cls
              targets_dict['rcnn_reg'] = rcnn_reg
  
              self.forward_ret_dict = targets_dict
          return batch_dict
  