import numpy as np import torch import torch.nn as nn from ..backbones_2d import BaseBEVBackbone from .anchor_head_template import AnchorHeadTemplate class SingleHead(BaseBEVBackbone): def __init__(self, model_cfg, input_channels, num_class, num_anchors_per_location, code_size, rpn_head_cfg=None, head_label_indices=None, separate_reg_config=None): super().__init__(rpn_head_cfg, input_channels) self.num_anchors_per_location = num_anchors_per_location self.num_class = num_class self.code_size = code_size self.model_cfg = model_cfg self.separate_reg_config = separate_reg_config self.register_buffer('head_label_indices', head_label_indices) if self.separate_reg_config is not None: code_size_cnt = 0 self.conv_box = nn.ModuleDict() self.conv_box_names = [] num_middle_conv = self.separate_reg_config.NUM_MIDDLE_CONV num_middle_filter = self.separate_reg_config.NUM_MIDDLE_FILTER conv_cls_list = [] c_in = input_channels for k in range(num_middle_conv): conv_cls_list.extend([ nn.Conv2d( c_in, num_middle_filter, kernel_size=3, stride=1, padding=1, bias=False ), nn.BatchNorm2d(num_middle_filter), nn.ReLU() ]) c_in = num_middle_filter conv_cls_list.append(nn.Conv2d( c_in, self.num_anchors_per_location * self.num_class, kernel_size=3, stride=1, padding=1 )) self.conv_cls = nn.Sequential(*conv_cls_list) for reg_config in self.separate_reg_config.REG_LIST: reg_name, reg_channel = reg_config.split(':') reg_channel = int(reg_channel) cur_conv_list = [] c_in = input_channels for k in range(num_middle_conv): cur_conv_list.extend([ nn.Conv2d( c_in, num_middle_filter, kernel_size=3, stride=1, padding=1, bias=False ), nn.BatchNorm2d(num_middle_filter), nn.ReLU() ]) c_in = num_middle_filter cur_conv_list.append(nn.Conv2d( c_in, self.num_anchors_per_location * int(reg_channel), kernel_size=3, stride=1, padding=1, bias=True )) code_size_cnt += reg_channel self.conv_box[f'conv_{reg_name}'] = nn.Sequential(*cur_conv_list) self.conv_box_names.append(f'conv_{reg_name}') for m in self.conv_box.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) assert code_size_cnt == code_size, f'Code size does not match: {code_size_cnt}:{code_size}' else: self.conv_cls = nn.Conv2d( input_channels, self.num_anchors_per_location * self.num_class, kernel_size=1 ) self.conv_box = nn.Conv2d( input_channels, self.num_anchors_per_location * self.code_size, kernel_size=1 ) if self.model_cfg.get('USE_DIRECTION_CLASSIFIER', None) is not None: self.conv_dir_cls = nn.Conv2d( input_channels, self.num_anchors_per_location * self.model_cfg.NUM_DIR_BINS, kernel_size=1 ) else: self.conv_dir_cls = None self.use_multihead = self.model_cfg.get('USE_MULTIHEAD', False) self.init_weights() def init_weights(self): pi = 0.01 if isinstance(self.conv_cls, nn.Conv2d): nn.init.constant_(self.conv_cls.bias, -np.log((1 - pi) / pi)) else: nn.init.constant_(self.conv_cls[-1].bias, -np.log((1 - pi) / pi)) def forward(self, spatial_features_2d): ret_dict = {} spatial_features_2d = super().forward({'spatial_features': spatial_features_2d})['spatial_features_2d'] cls_preds = self.conv_cls(spatial_features_2d) if self.separate_reg_config is None: box_preds = self.conv_box(spatial_features_2d) else: box_preds_list = [] for reg_name in self.conv_box_names: box_preds_list.append(self.conv_box[reg_name](spatial_features_2d)) box_preds = torch.cat(box_preds_list, dim=1) if not self.use_multihead: box_preds = box_preds.permute(0, 2, 3, 1).contiguous() cls_preds = cls_preds.permute(0, 2, 3, 1).contiguous() else: H, W = box_preds.shape[2:] batch_size = box_preds.shape[0] box_preds = box_preds.view(-1, self.num_anchors_per_location, self.code_size, H, W).permute(0, 1, 3, 4, 2).contiguous() cls_preds = cls_preds.view(-1, self.num_anchors_per_location, self.num_class, H, W).permute(0, 1, 3, 4, 2).contiguous() box_preds = box_preds.view(batch_size, -1, self.code_size) cls_preds = cls_preds.view(batch_size, -1, self.num_class) if self.conv_dir_cls is not None: dir_cls_preds = self.conv_dir_cls(spatial_features_2d) if self.use_multihead: dir_cls_preds = dir_cls_preds.view( -1, self.num_anchors_per_location, self.model_cfg.NUM_DIR_BINS, H, W).permute(0, 1, 3, 4, 2).contiguous() dir_cls_preds = dir_cls_preds.view(batch_size, -1, self.model_cfg.NUM_DIR_BINS) else: dir_cls_preds = dir_cls_preds.permute(0, 2, 3, 1).contiguous() else: dir_cls_preds = None ret_dict['cls_preds'] = cls_preds ret_dict['box_preds'] = box_preds ret_dict['dir_cls_preds'] = dir_cls_preds return ret_dict class AnchorHeadMulti(AnchorHeadTemplate): def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range, predict_boxes_when_training=True, **kwargs): super().__init__( model_cfg=model_cfg, num_class=num_class, class_names=class_names, grid_size=grid_size, point_cloud_range=point_cloud_range, predict_boxes_when_training=predict_boxes_when_training ) self.model_cfg = model_cfg self.separate_multihead = self.model_cfg.get('SEPARATE_MULTIHEAD', False) if self.model_cfg.get('SHARED_CONV_NUM_FILTER', None) is not None: shared_conv_num_filter = self.model_cfg.SHARED_CONV_NUM_FILTER self.shared_conv = nn.Sequential( nn.Conv2d(input_channels, shared_conv_num_filter, 3, stride=1, padding=1, bias=False), nn.BatchNorm2d(shared_conv_num_filter, eps=1e-3, momentum=0.01), nn.ReLU(), ) else: self.shared_conv = None shared_conv_num_filter = input_channels self.rpn_heads = None self.make_multihead(shared_conv_num_filter) def make_multihead(self, input_channels): rpn_head_cfgs = self.model_cfg.RPN_HEAD_CFGS rpn_heads = [] class_names = [] for rpn_head_cfg in rpn_head_cfgs: class_names.extend(rpn_head_cfg['HEAD_CLS_NAME']) for rpn_head_cfg in rpn_head_cfgs: num_anchors_per_location = sum([self.num_anchors_per_location[class_names.index(head_cls)] for head_cls in rpn_head_cfg['HEAD_CLS_NAME']]) head_label_indices = torch.from_numpy(np.array([ self.class_names.index(cur_name) + 1 for cur_name in rpn_head_cfg['HEAD_CLS_NAME'] ])) rpn_head = SingleHead( self.model_cfg, input_channels, len(rpn_head_cfg['HEAD_CLS_NAME']) if self.separate_multihead else self.num_class, num_anchors_per_location, self.box_coder.code_size, rpn_head_cfg, head_label_indices=head_label_indices, separate_reg_config=self.model_cfg.get('SEPARATE_REG_CONFIG', None) ) rpn_heads.append(rpn_head) self.rpn_heads = nn.ModuleList(rpn_heads) def forward(self, data_dict): spatial_features_2d = data_dict['spatial_features_2d'] if self.shared_conv is not None: spatial_features_2d = self.shared_conv(spatial_features_2d) ret_dicts = [] for rpn_head in self.rpn_heads: ret_dicts.append(rpn_head(spatial_features_2d)) cls_preds = [ret_dict['cls_preds'] for ret_dict in ret_dicts] box_preds = [ret_dict['box_preds'] for ret_dict in ret_dicts] ret = { 'cls_preds': cls_preds if self.separate_multihead else torch.cat(cls_preds, dim=1), 'box_preds': box_preds if self.separate_multihead else torch.cat(box_preds, dim=1), } if self.model_cfg.get('USE_DIRECTION_CLASSIFIER', False): dir_cls_preds = [ret_dict['dir_cls_preds'] for ret_dict in ret_dicts] ret['dir_cls_preds'] = dir_cls_preds if self.separate_multihead else torch.cat(dir_cls_preds, dim=1) self.forward_ret_dict.update(ret) if self.training: targets_dict = self.assign_targets( gt_boxes=data_dict['gt_boxes'] ) self.forward_ret_dict.update(targets_dict) if not self.training or self.predict_boxes_when_training: batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( batch_size=data_dict['batch_size'], cls_preds=ret['cls_preds'], box_preds=ret['box_preds'], dir_cls_preds=ret.get('dir_cls_preds', None) ) if isinstance(batch_cls_preds, list): multihead_label_mapping = [] for idx in range(len(batch_cls_preds)): multihead_label_mapping.append(self.rpn_heads[idx].head_label_indices) data_dict['multihead_label_mapping'] = multihead_label_mapping data_dict['batch_cls_preds'] = batch_cls_preds data_dict['batch_box_preds'] = batch_box_preds data_dict['cls_preds_normalized'] = False return data_dict def get_cls_layer_loss(self): loss_weights = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS if 'pos_cls_weight' in loss_weights: pos_cls_weight = loss_weights['pos_cls_weight'] neg_cls_weight = loss_weights['neg_cls_weight'] else: pos_cls_weight = neg_cls_weight = 1.0 cls_preds = self.forward_ret_dict['cls_preds'] box_cls_labels = self.forward_ret_dict['box_cls_labels'] if not isinstance(cls_preds, list): cls_preds = [cls_preds] batch_size = int(cls_preds[0].shape[0]) cared = box_cls_labels >= 0 # [N, num_anchors] positives = box_cls_labels > 0 negatives = box_cls_labels == 0 negative_cls_weights = negatives * 1.0 * neg_cls_weight cls_weights = (negative_cls_weights + pos_cls_weight * positives).float() reg_weights = positives.float() if self.num_class == 1: # class agnostic box_cls_labels[positives] = 1 pos_normalizer = positives.sum(1, keepdim=True).float() reg_weights /= torch.clamp(pos_normalizer, min=1.0) cls_weights /= torch.clamp(pos_normalizer, min=1.0) cls_targets = box_cls_labels * cared.type_as(box_cls_labels) one_hot_targets = torch.zeros( *list(cls_targets.shape), self.num_class + 1, dtype=cls_preds[0].dtype, device=cls_targets.device ) one_hot_targets.scatter_(-1, cls_targets.unsqueeze(dim=-1).long(), 1.0) one_hot_targets = one_hot_targets[..., 1:] start_idx = c_idx = 0 cls_losses = 0 for idx, cls_pred in enumerate(cls_preds): cur_num_class = self.rpn_heads[idx].num_class cls_pred = cls_pred.view(batch_size, -1, cur_num_class) if self.separate_multihead: one_hot_target = one_hot_targets[:, start_idx:start_idx + cls_pred.shape[1], c_idx:c_idx + cur_num_class] c_idx += cur_num_class else: one_hot_target = one_hot_targets[:, start_idx:start_idx + cls_pred.shape[1]] cls_weight = cls_weights[:, start_idx:start_idx + cls_pred.shape[1]] cls_loss_src = self.cls_loss_func(cls_pred, one_hot_target, weights=cls_weight) # [N, M] cls_loss = cls_loss_src.sum() / batch_size cls_loss = cls_loss * loss_weights['cls_weight'] cls_losses += cls_loss start_idx += cls_pred.shape[1] assert start_idx == one_hot_targets.shape[1] tb_dict = { 'rpn_loss_cls': cls_losses.item() } return cls_losses, tb_dict def get_box_reg_layer_loss(self): box_preds = self.forward_ret_dict['box_preds'] box_dir_cls_preds = self.forward_ret_dict.get('dir_cls_preds', None) box_reg_targets = self.forward_ret_dict['box_reg_targets'] box_cls_labels = self.forward_ret_dict['box_cls_labels'] positives = box_cls_labels > 0 reg_weights = positives.float() pos_normalizer = positives.sum(1, keepdim=True).float() reg_weights /= torch.clamp(pos_normalizer, min=1.0) if not isinstance(box_preds, list): box_preds = [box_preds] batch_size = int(box_preds[0].shape[0]) if isinstance(self.anchors, list): if self.use_multihead: anchors = torch.cat( [anchor.permute(3, 4, 0, 1, 2, 5).contiguous().view(-1, anchor.shape[-1]) for anchor in self.anchors], dim=0 ) else: anchors = torch.cat(self.anchors, dim=-3) else: anchors = self.anchors anchors = anchors.view(1, -1, anchors.shape[-1]).repeat(batch_size, 1, 1) start_idx = 0 box_losses = 0 tb_dict = {} for idx, box_pred in enumerate(box_preds): box_pred = box_pred.view( batch_size, -1, box_pred.shape[-1] // self.num_anchors_per_location if not self.use_multihead else box_pred.shape[-1] ) box_reg_target = box_reg_targets[:, start_idx:start_idx + box_pred.shape[1]] reg_weight = reg_weights[:, start_idx:start_idx + box_pred.shape[1]] # sin(a - b) = sinacosb-cosasinb if box_dir_cls_preds is not None: box_pred_sin, reg_target_sin = self.add_sin_difference(box_pred, box_reg_target) loc_loss_src = self.reg_loss_func(box_pred_sin, reg_target_sin, weights=reg_weight) # [N, M] else: loc_loss_src = self.reg_loss_func(box_pred, box_reg_target, weights=reg_weight) # [N, M] loc_loss = loc_loss_src.sum() / batch_size loc_loss = loc_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['loc_weight'] box_losses += loc_loss tb_dict['rpn_loss_loc'] = tb_dict.get('rpn_loss_loc', 0) + loc_loss.item() if box_dir_cls_preds is not None: if not isinstance(box_dir_cls_preds, list): box_dir_cls_preds = [box_dir_cls_preds] dir_targets = self.get_direction_target( anchors, box_reg_targets, dir_offset=self.model_cfg.DIR_OFFSET, num_bins=self.model_cfg.NUM_DIR_BINS ) box_dir_cls_pred = box_dir_cls_preds[idx] dir_logit = box_dir_cls_pred.view(batch_size, -1, self.model_cfg.NUM_DIR_BINS) weights = positives.type_as(dir_logit) weights /= torch.clamp(weights.sum(-1, keepdim=True), min=1.0) weight = weights[:, start_idx:start_idx + box_pred.shape[1]] dir_target = dir_targets[:, start_idx:start_idx + box_pred.shape[1]] dir_loss = self.dir_loss_func(dir_logit, dir_target, weights=weight) dir_loss = dir_loss.sum() / batch_size dir_loss = dir_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['dir_weight'] box_losses += dir_loss tb_dict['rpn_loss_dir'] = tb_dict.get('rpn_loss_dir', 0) + dir_loss.item() start_idx += box_pred.shape[1] return box_losses, tb_dict