import torch import torch.nn as nn from ...ops.pointnet2.pointnet2_stack import voxel_pool_modules as voxelpool_stack_modules from ...utils import common_utils from .roi_head_template import RoIHeadTemplate class VoxelRCNNHead(RoIHeadTemplate): def __init__(self, backbone_channels, model_cfg, point_cloud_range, voxel_size, num_class=1, **kwargs): super().__init__(num_class=num_class, model_cfg=model_cfg) self.model_cfg = model_cfg self.pool_cfg = model_cfg.ROI_GRID_POOL LAYER_cfg = self.pool_cfg.POOL_LAYERS self.point_cloud_range = point_cloud_range self.voxel_size = voxel_size c_out = 0 self.roi_grid_pool_layers = nn.ModuleList() for src_name in self.pool_cfg.FEATURES_SOURCE: mlps = LAYER_cfg[src_name].MLPS for k in range(len(mlps)): mlps[k] = [backbone_channels[src_name]] + mlps[k] pool_layer = voxelpool_stack_modules.NeighborVoxelSAModuleMSG( query_ranges=LAYER_cfg[src_name].QUERY_RANGES, nsamples=LAYER_cfg[src_name].NSAMPLE, radii=LAYER_cfg[src_name].POOL_RADIUS, mlps=mlps, pool_method=LAYER_cfg[src_name].POOL_METHOD, ) self.roi_grid_pool_layers.append(pool_layer) c_out += sum([x[-1] for x in mlps]) GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE # c_out = sum([x[-1] for x in mlps]) pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * c_out shared_fc_list = [] for k in range(0, self.model_cfg.SHARED_FC.__len__()): shared_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.SHARED_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]), nn.ReLU(inplace=True) ]) 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) cls_fc_list = [] for k in range(0, self.model_cfg.CLS_FC.__len__()): cls_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.CLS_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.CLS_FC[k]), nn.ReLU() ]) pre_channel = self.model_cfg.CLS_FC[k] if k != self.model_cfg.CLS_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: cls_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.cls_fc_layers = nn.Sequential(*cls_fc_list) self.cls_pred_layer = nn.Linear(pre_channel, self.num_class, bias=True) reg_fc_list = [] for k in range(0, self.model_cfg.REG_FC.__len__()): reg_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.REG_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.REG_FC[k]), nn.ReLU() ]) pre_channel = self.model_cfg.REG_FC[k] if k != self.model_cfg.REG_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: reg_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.reg_fc_layers = nn.Sequential(*reg_fc_list) self.reg_pred_layer = nn.Linear(pre_channel, self.box_coder.code_size * self.num_class, bias=True) self.init_weights() def init_weights(self): init_func = nn.init.xavier_normal_ for module_list in [self.shared_fc_layer, self.cls_fc_layers, self.reg_fc_layers]: for m in module_list.modules(): if isinstance(m, nn.Linear): init_func(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) nn.init.normal_(self.cls_pred_layer.weight, 0, 0.01) nn.init.constant_(self.cls_pred_layer.bias, 0) nn.init.normal_(self.reg_pred_layer.weight, mean=0, std=0.001) nn.init.constant_(self.reg_pred_layer.bias, 0) # def _init_weights(self): # init_func = nn.init.xavier_normal_ # for m in self.modules(): # if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear): # 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 roi_grid_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: """ rois = batch_dict['rois'] batch_size = batch_dict['batch_size'] with_vf_transform = batch_dict.get('with_voxel_feature_transform', False) roi_grid_xyz, _ = self.get_global_grid_points_of_roi( rois, grid_size=self.pool_cfg.GRID_SIZE ) # (BxN, 6x6x6, 3) # roi_grid_xyz: (B, Nx6x6x6, 3) roi_grid_xyz = roi_grid_xyz.view(batch_size, -1, 3) # compute the voxel coordinates of grid points roi_grid_coords_x = (roi_grid_xyz[:, :, 0:1] - self.point_cloud_range[0]) // self.voxel_size[0] roi_grid_coords_y = (roi_grid_xyz[:, :, 1:2] - self.point_cloud_range[1]) // self.voxel_size[1] roi_grid_coords_z = (roi_grid_xyz[:, :, 2:3] - self.point_cloud_range[2]) // self.voxel_size[2] # roi_grid_coords: (B, Nx6x6x6, 3) roi_grid_coords = torch.cat([roi_grid_coords_x, roi_grid_coords_y, roi_grid_coords_z], dim=-1) batch_idx = rois.new_zeros(batch_size, roi_grid_coords.shape[1], 1) for bs_idx in range(batch_size): batch_idx[bs_idx, :, 0] = bs_idx # roi_grid_coords: (B, Nx6x6x6, 4) # roi_grid_coords = torch.cat([batch_idx, roi_grid_coords], dim=-1) # roi_grid_coords = roi_grid_coords.int() roi_grid_batch_cnt = rois.new_zeros(batch_size).int().fill_(roi_grid_coords.shape[1]) pooled_features_list = [] for k, src_name in enumerate(self.pool_cfg.FEATURES_SOURCE): pool_layer = self.roi_grid_pool_layers[k] cur_stride = batch_dict['multi_scale_3d_strides'][src_name] cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name] if with_vf_transform: cur_sp_tensors = batch_dict['multi_scale_3d_features_post'][src_name] else: cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name] # compute voxel center xyz and batch_cnt cur_coords = cur_sp_tensors.indices cur_voxel_xyz = common_utils.get_voxel_centers( cur_coords[:, 1:4], downsample_times=cur_stride, voxel_size=self.voxel_size, point_cloud_range=self.point_cloud_range ) cur_voxel_xyz_batch_cnt = cur_voxel_xyz.new_zeros(batch_size).int() for bs_idx in range(batch_size): cur_voxel_xyz_batch_cnt[bs_idx] = (cur_coords[:, 0] == bs_idx).sum() # get voxel2point tensor v2p_ind_tensor = common_utils.generate_voxel2pinds(cur_sp_tensors) # compute the grid coordinates in this scale, in [batch_idx, x y z] order cur_roi_grid_coords = roi_grid_coords // cur_stride cur_roi_grid_coords = torch.cat([batch_idx, cur_roi_grid_coords], dim=-1) cur_roi_grid_coords = cur_roi_grid_coords.int() # voxel neighbor aggregation pooled_features = pool_layer( xyz=cur_voxel_xyz.contiguous(), xyz_batch_cnt=cur_voxel_xyz_batch_cnt, new_xyz=roi_grid_xyz.contiguous().view(-1, 3), new_xyz_batch_cnt=roi_grid_batch_cnt, new_coords=cur_roi_grid_coords.contiguous().view(-1, 4), features=cur_sp_tensors.features.contiguous(), voxel2point_indices=v2p_ind_tensor ) pooled_features = pooled_features.view( -1, self.pool_cfg.GRID_SIZE ** 3, pooled_features.shape[-1] ) # (BxN, 6x6x6, C) pooled_features_list.append(pooled_features) ms_pooled_features = torch.cat(pooled_features_list, dim=-1) return ms_pooled_features def get_global_grid_points_of_roi(self, rois, grid_size): rois = rois.view(-1, rois.shape[-1]) batch_size_rcnn = rois.shape[0] local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size) # (B, 6x6x6, 3) global_roi_grid_points = common_utils.rotate_points_along_z( local_roi_grid_points.clone(), rois[:, 6] ).squeeze(dim=1) global_center = rois[:, 0:3].clone() global_roi_grid_points += global_center.unsqueeze(dim=1) return global_roi_grid_points, local_roi_grid_points @staticmethod def get_dense_grid_points(rois, batch_size_rcnn, grid_size): faked_features = rois.new_ones((grid_size, grid_size, grid_size)) dense_idx = faked_features.nonzero() # (N, 3) [x_idx, y_idx, z_idx] dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float() # (B, 6x6x6, 3) local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6] roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \ - (local_roi_size.unsqueeze(dim=1) / 2) # (B, 6x6x6, 3) return roi_grid_points def forward(self, batch_dict): """ :param input_data: input dict :return: """ 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_features = self.roi_grid_pool(batch_dict) # (BxN, 6x6x6, C) # Box Refinement pooled_features = pooled_features.view(pooled_features.size(0), -1) shared_features = self.shared_fc_layer(pooled_features) rcnn_cls = self.cls_pred_layer(self.cls_fc_layers(shared_features)) rcnn_reg = self.reg_pred_layer(self.reg_fc_layers(shared_features)) # grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE # batch_size_rcnn = pooled_features.shape[0] # pooled_features = pooled_features.permute(0, 2, 1).\ # contiguous().view(batch_size_rcnn, -1, grid_size, grid_size, grid_size) # (BxN, C, 6, 6, 6) # shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1)) # rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) # rcnn_reg = self.reg_layers(shared_features).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