# Modified from https://github.com/fundamentalvision/BEVFormer. # Copyright (c) Alibaba, Inc. and its affiliates. import copy import torch import torch.nn as nn from mmcv.cnn import Linear, bias_init_with_prob, build_activation_layer from mmcv.utils import digit_version from easycv.core.bbox.bbox_util import normalize_bbox from easycv.core.bbox.builder import (build_bbox_assigner, build_bbox_coder, build_bbox_sampler) from easycv.core.utils.misc import multi_apply from easycv.models.builder import (build_loss, build_positional_encoding, build_transformer) from easycv.models.detection.dense_heads.anchor_free_head import AnchorFreeHead from easycv.models.detection.utils.misc import inverse_sigmoid from easycv.models.registry import HEADS from easycv.models.utils.dist_utils import reduce_mean @HEADS.register_module() class BEVFormerHead(AnchorFreeHead): """Head of Detr3D. Args: with_box_refine (bool): Whether to refine the reference points in the decoder. Defaults to False. as_two_stage (bool) : Whether to generate the proposal from the outputs of encoder. transformer (obj:`ConfigDict`): ConfigDict is used for building the Encoder and Decoder. bev_h, bev_w (int): spatial shape of BEV queries. """ def __init__(self, num_classes, in_channels, num_query=100, num_query_one2many=0, one2many_gt_mul=None, num_reg_fcs=2, with_box_refine=False, as_two_stage=False, transformer=None, bbox_coder=None, num_cls_fcs=2, code_weights=None, bev_h=30, bev_w=30, sync_cls_avg_factor=False, positional_encoding=dict( type='SinePositionalEncoding', num_feats=128, normalize=True), loss_cls=dict( type='CrossEntropyLoss', bg_cls_weight=0.1, use_sigmoid=False, loss_weight=1.0, class_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=5.0), loss_iou=dict(type='GIoULoss', loss_weight=2.0), train_cfg=dict( assigner=dict( type='HungarianAssigner', cls_cost=dict(type='ClassificationCost', weight=1.), reg_cost=dict(type='BBoxL1Cost', weight=5.0), iou_cost=dict( type='IoUCost', iou_mode='giou', weight=2.0))), test_cfg=dict(max_per_img=100), init_cfg=None, **kwargs): self.bev_h = bev_h self.bev_w = bev_w self.with_box_refine = with_box_refine self.as_two_stage = as_two_stage if self.as_two_stage: transformer['as_two_stage'] = self.as_two_stage if 'code_size' in kwargs: self.code_size = kwargs['code_size'] else: self.code_size = 10 if code_weights is not None: self.code_weights = code_weights else: self.code_weights = [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2 ] self.bbox_coder = build_bbox_coder(bbox_coder) self.pc_range = self.bbox_coder.pc_range self.real_w = self.pc_range[3] - self.pc_range[0] self.real_h = self.pc_range[4] - self.pc_range[1] self.num_cls_fcs = num_cls_fcs - 1 super(AnchorFreeHead, self).__init__(init_cfg) self.bg_cls_weight = 0 self.sync_cls_avg_factor = sync_cls_avg_factor class_weight = loss_cls.get('class_weight', None) if class_weight is not None and (self.__class__ is BEVFormerHead): assert isinstance(class_weight, float), 'Expected ' \ 'class_weight to have type float. Found ' \ f'{type(class_weight)}.' # NOTE following the official DETR rep0, bg_cls_weight means # relative classification weight of the no-object class. bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight) assert isinstance(bg_cls_weight, float), 'Expected ' \ 'bg_cls_weight to have type float. Found ' \ f'{type(bg_cls_weight)}.' class_weight = torch.ones(num_classes + 1) * class_weight # set background class as the last indice class_weight[num_classes] = bg_cls_weight loss_cls.update({'class_weight': class_weight}) if 'bg_cls_weight' in loss_cls: loss_cls.pop('bg_cls_weight') self.bg_cls_weight = bg_cls_weight if train_cfg: assert 'assigner' in train_cfg, 'assigner should be provided '\ 'when train_cfg is set.' assigner = train_cfg['assigner'] assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \ 'The classification weight for loss and matcher should be' \ 'exactly the same.' assert loss_bbox['loss_weight'] == assigner['reg_cost'][ 'weight'], 'The regression L1 weight for loss and matcher ' \ 'should be exactly the same.' assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \ 'The regression iou weight for loss and matcher should be' \ 'exactly the same.' self.assigner = build_bbox_assigner(assigner) # sampling=False, so use PseudoBBoxSampler sampler_cfg = dict(type='PseudoBBoxSampler') self.sampler = build_bbox_sampler(sampler_cfg, context=self) # for one2many task self.num_query_one2many = num_query_one2many self.num_query_one2one = num_query self.one2many_gt_mul = one2many_gt_mul self.num_query = num_query + num_query_one2many if num_query_one2many > 0 else num_query self.num_classes = num_classes self.in_channels = in_channels self.num_reg_fcs = num_reg_fcs self.train_cfg = train_cfg self.test_cfg = test_cfg self.loss_cls = build_loss(loss_cls) self.loss_bbox = build_loss(loss_bbox) self.loss_iou = build_loss(loss_iou) if self.loss_cls.use_sigmoid: self.cls_out_channels = num_classes else: self.cls_out_channels = num_classes + 1 self.act_cfg = transformer.get('act_cfg', dict(type='ReLU', inplace=True)) self.activate = build_activation_layer(self.act_cfg) self.positional_encoding = build_positional_encoding( positional_encoding) self.transformer = build_transformer(transformer) self.embed_dims = self.transformer.embed_dims assert 'num_feats' in positional_encoding num_feats = positional_encoding['num_feats'] assert num_feats * 2 == self.embed_dims, 'embed_dims should' \ f' be exactly 2 times of num_feats. Found {self.embed_dims}' \ f' and {num_feats}.' self._init_layers() self.code_weights = nn.Parameter( torch.tensor(self.code_weights, requires_grad=False), requires_grad=False) def _init_layers(self): """Initialize classification branch and regression branch of head.""" cls_branch = [] for _ in range(self.num_reg_fcs): cls_branch.append(Linear(self.embed_dims, self.embed_dims)) cls_branch.append(nn.LayerNorm(self.embed_dims)) cls_branch.append(nn.ReLU(inplace=True)) cls_branch.append(Linear(self.embed_dims, self.cls_out_channels)) fc_cls = nn.Sequential(*cls_branch) reg_branch = [] for _ in range(self.num_reg_fcs): reg_branch.append(Linear(self.embed_dims, self.embed_dims)) reg_branch.append(nn.ReLU()) reg_branch.append(Linear(self.embed_dims, self.code_size)) reg_branch = nn.Sequential(*reg_branch) def _get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) # last reg_branch is used to generate proposal from # encode feature map when as_two_stage is True. num_pred = (self.transformer.decoder.num_layers + 1) if \ self.as_two_stage else self.transformer.decoder.num_layers if self.with_box_refine: self.cls_branches = _get_clones(fc_cls, num_pred) self.reg_branches = _get_clones(reg_branch, num_pred) else: self.cls_branches = nn.ModuleList( [fc_cls for _ in range(num_pred)]) self.reg_branches = nn.ModuleList( [reg_branch for _ in range(num_pred)]) if not self.as_two_stage: self.bev_embedding = nn.Embedding(self.bev_h * self.bev_w, self.embed_dims) self.query_embedding = nn.Embedding(self.num_query, self.embed_dims * 2) def init_weights(self): """Initialize weights of the DeformDETR head.""" self.transformer.init_weights() if self.loss_cls.use_sigmoid: bias_init = bias_init_with_prob(0.01) for m in self.cls_branches: nn.init.constant_(m[-1].bias, bias_init) def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): """load checkpoints.""" # Names of some parameters in has been changed. version = local_metadata.get('version', None) if (version is None or version < 2) and self.__class__ is BEVFormerHead: convert_dict = { '.self_attn.': '.attentions.0.', '.ffn.': '.ffns.0.', '.multihead_attn.': '.attentions.1.', '.decoder.norm.': '.decoder.post_norm.' } state_dict_keys = list(state_dict.keys()) for k in state_dict_keys: for ori_key, convert_key in convert_dict.items(): if ori_key in k: convert_key = k.replace(ori_key, convert_key) state_dict[convert_key] = state_dict[k] del state_dict[k] super(BEVFormerHead, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) def forward(self, mlvl_feats, img_metas, prev_bev=None, only_bev=False): """Forward function. Args: mlvl_feats (tuple[Tensor]): Features from the upstream network, each is a 5D-tensor with shape (B, N, C, H, W). prev_bev: previous bev featues only_bev: only compute BEV features with encoder. Returns: all_cls_scores (Tensor): Outputs from the classification head, \ shape [nb_dec, bs, num_query, cls_out_channels]. Note \ cls_out_channels should includes background. all_bbox_preds (Tensor): Sigmoid outputs from the regression \ head with normalized coordinate format (cx, cy, w, l, cz, h, theta, vx, vy). \ Shape [nb_dec, bs, num_query, 9]. """ bs, num_cam, _, _, _ = mlvl_feats[0].shape dtype = mlvl_feats[0].dtype object_query_embeds = self.query_embedding.weight.to(dtype) bev_queries = self.bev_embedding.weight.to(dtype) bev_mask = torch.zeros((bs, self.bev_h, self.bev_w), device=bev_queries.device).to(dtype) bev_pos = self.positional_encoding(bev_mask).to(dtype) if only_bev: # only use encoder to obtain BEV features, TODO: refine the workaround return self.transformer.get_bev_features( mlvl_feats, bev_queries, self.bev_h, self.bev_w, grid_length=(self.real_h / self.bev_h, self.real_w / self.bev_w), bev_pos=bev_pos, img_metas=img_metas, prev_bev=prev_bev, ) else: # make attn mask for one2many task self_attn_mask = torch.zeros([ self.num_query, self.num_query, ]).bool().to(bev_queries.device) self_attn_mask[self.num_query_one2one:, 0:self.num_query_one2one, ] = True self_attn_mask[0:self.num_query_one2one, self.num_query_one2one:, ] = True outputs = self.transformer( mlvl_feats, bev_queries, object_query_embeds, self.bev_h, self.bev_w, grid_length=(self.real_h / self.bev_h, self.real_w / self.bev_w), bev_pos=bev_pos, reg_branches=self.reg_branches if self.with_box_refine else None, # noqa:E501 cls_branches=self.cls_branches if self.as_two_stage else None, img_metas=img_metas, prev_bev=prev_bev, attn_mask=self_attn_mask) bev_embed, hs, init_reference, inter_references = outputs hs = hs.permute(0, 2, 1, 3) outputs_classes = [] outputs_coords = [] for lvl in range(hs.shape[0]): if lvl == 0: reference = init_reference else: reference = inter_references[lvl - 1] reference = inverse_sigmoid(reference) outputs_class = self.cls_branches[lvl](hs[lvl]) tmp = self.reg_branches[lvl](hs[lvl]) # TODO: check the shape of reference assert reference.shape[-1] == 3 # tmp: torch.Size([1, 900, 10]) # tmp[..., 0:2] += reference[..., 0:2] # tmp[..., 0:2] = tmp[..., 0:2].sigmoid() # tmp[..., 4:5] += reference[..., 2:3] # tmp[..., 4:5] = tmp[..., 4:5].sigmoid() # tmp[..., 0:1] = ( # tmp[..., 0:1] * (self.pc_range[3] - self.pc_range[0]) + # self.pc_range[0]) # tmp[..., 1:2] = ( # tmp[..., 1:2] * (self.pc_range[4] - self.pc_range[1]) + # self.pc_range[1]) # tmp[..., 4:5] = ( # tmp[..., 4:5] * (self.pc_range[5] - self.pc_range[2]) + # self.pc_range[2]) # remove inplace operation, metric may incorrect when using blade tmp_0_2 = tmp[..., 0:2] tmp_0_2_add_reference = tmp_0_2 + reference[..., 0:2] tmp_0_2_add_reference = tmp_0_2_add_reference.sigmoid() tmp_4_5 = tmp[..., 4:5] tmp_4_5_add_reference = tmp_4_5 + reference[..., 2:3] tmp_4_5_add_reference = tmp_4_5_add_reference.sigmoid() tmp_0_1 = tmp_0_2_add_reference[..., 0:1] tmp_0_1_new = ( tmp_0_1 * (self.pc_range[3] - self.pc_range[0]) + self.pc_range[0]) tmp_1_2 = tmp_0_2_add_reference[..., 1:2] tmp_1_2_new = ( tmp_1_2 * (self.pc_range[4] - self.pc_range[1]) + self.pc_range[1]) tmp_4_5_new = ( tmp_4_5_add_reference * (self.pc_range[5] - self.pc_range[2]) + self.pc_range[2]) tmp_2_4 = tmp[..., 2:4] tmp_5_10 = tmp[..., 5:10] tmp = torch.cat( [tmp_0_1_new, tmp_1_2_new, tmp_2_4, tmp_4_5_new, tmp_5_10], dim=-1) # TODO: check if using sigmoid outputs_coord = tmp outputs_classes.append(outputs_class) outputs_coords.append(outputs_coord) outputs_classes = torch.stack(outputs_classes) outputs_coords = torch.stack(outputs_coords) outs = { 'bev_embed': bev_embed, 'all_cls_scores': outputs_classes[:, :, :self.num_query_one2one, :], 'all_bbox_preds': outputs_coords[:, :, :self.num_query_one2one, :], 'enc_cls_scores': None, 'enc_bbox_preds': None, } if self.num_query_one2many > 0: outs['all_cls_scores_aux'] = outputs_classes[:, :, self. num_query_one2one:, :] outs['all_bbox_preds_aux'] = outputs_coords[:, :, self. num_query_one2one:, :] return outs def _get_target_single(self, cls_score, bbox_pred, gt_labels, gt_bboxes, gt_bboxes_ignore=None): """"Compute regression and classification targets for one image. Outputs from a single decoder layer of a single feature level are used. Args: cls_score (Tensor): Box score logits from a single decoder layer for one image. Shape [num_query, cls_out_channels]. bbox_pred (Tensor): Sigmoid outputs from a single decoder layer for one image, with normalized coordinate (cx, cy, w, h) and shape [num_query, 4]. gt_bboxes (Tensor): Ground truth bboxes for one image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels (Tensor): Ground truth class indices for one image with shape (num_gts, ). gt_bboxes_ignore (Tensor, optional): Bounding boxes which can be ignored. Default None. Returns: tuple[Tensor]: a tuple containing the following for one image. - labels (Tensor): Labels of each image. - label_weights (Tensor]): Label weights of each image. - bbox_targets (Tensor): BBox targets of each image. - bbox_weights (Tensor): BBox weights of each image. - pos_inds (Tensor): Sampled positive indices for each image. - neg_inds (Tensor): Sampled negative indices for each image. """ num_bboxes = bbox_pred.size(0) # assigner and sampler gt_c = gt_bboxes.shape[-1] assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes, gt_labels, gt_bboxes_ignore) sampling_result = self.sampler.sample(assign_result, bbox_pred, gt_bboxes) pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds # label targets labels = gt_bboxes.new_full((num_bboxes, ), self.num_classes, dtype=torch.long) labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds] label_weights = gt_bboxes.new_ones(num_bboxes) # bbox targets bbox_targets = torch.zeros_like(bbox_pred)[..., :gt_c] bbox_weights = torch.zeros_like(bbox_pred) bbox_weights[pos_inds] = 1.0 # DETR sampling_result.pos_gt_bboxes = sampling_result.pos_gt_bboxes.type_as( bbox_targets) bbox_targets[pos_inds] = sampling_result.pos_gt_bboxes return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, neg_inds) def get_targets(self, cls_scores_list, bbox_preds_list, gt_bboxes_list, gt_labels_list, gt_bboxes_ignore_list=None): """"Compute regression and classification targets for a batch image. Outputs from a single decoder layer of a single feature level are used. Args: cls_scores_list (list[Tensor]): Box score logits from a single decoder layer for each image with shape [num_query, cls_out_channels]. bbox_preds_list (list[Tensor]): Sigmoid outputs from a single decoder layer for each image, with normalized coordinate (cx, cy, w, h) and shape [num_query, 4]. gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels_list (list[Tensor]): Ground truth class indices for each image with shape (num_gts, ). gt_bboxes_ignore_list (list[Tensor], optional): Bounding boxes which can be ignored for each image. Default None. Returns: tuple: a tuple containing the following targets. - labels_list (list[Tensor]): Labels for all images. - label_weights_list (list[Tensor]): Label weights for all \ images. - bbox_targets_list (list[Tensor]): BBox targets for all \ images. - bbox_weights_list (list[Tensor]): BBox weights for all \ images. - num_total_pos (int): Number of positive samples in all \ images. - num_total_neg (int): Number of negative samples in all \ images. """ assert gt_bboxes_ignore_list is None, \ 'Only supports for gt_bboxes_ignore setting to None.' num_imgs = len(cls_scores_list) gt_bboxes_ignore_list = [ gt_bboxes_ignore_list for _ in range(num_imgs) ] (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply(self._get_target_single, cls_scores_list, bbox_preds_list, gt_labels_list, gt_bboxes_list, gt_bboxes_ignore_list) num_total_pos = sum((inds.numel() for inds in pos_inds_list)) num_total_neg = sum((inds.numel() for inds in neg_inds_list)) return (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) def loss_single(self, cls_scores, bbox_preds, gt_bboxes_list, gt_labels_list, gt_bboxes_ignore_list=None): """"Loss function for outputs from a single decoder layer of a single feature level. Args: cls_scores (Tensor): Box score logits from a single decoder layer for all images. Shape [bs, num_query, cls_out_channels]. bbox_preds (Tensor): Sigmoid outputs from a single decoder layer for all images, with normalized coordinate (cx, cy, w, h) and shape [bs, num_query, 4]. gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels_list (list[Tensor]): Ground truth class indices for each image with shape (num_gts, ). gt_bboxes_ignore_list (list[Tensor], optional): Bounding boxes which can be ignored for each image. Default None. Returns: dict[str, Tensor]: A dictionary of loss components for outputs from a single decoder layer. """ num_imgs = cls_scores.size(0) cls_scores_list = [cls_scores[i] for i in range(num_imgs)] bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)] cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list, gt_bboxes_list, gt_labels_list, gt_bboxes_ignore_list) (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets labels = torch.cat(labels_list, 0) label_weights = torch.cat(label_weights_list, 0) bbox_targets = torch.cat(bbox_targets_list, 0) bbox_weights = torch.cat(bbox_weights_list, 0) # classification loss cls_scores = cls_scores.reshape(-1, self.cls_out_channels) # construct weighted avg_factor to match with the official DETR repo cls_avg_factor = num_total_pos * 1.0 + \ num_total_neg * self.bg_cls_weight if self.sync_cls_avg_factor: cls_avg_factor = reduce_mean( cls_scores.new_tensor([cls_avg_factor])) cls_avg_factor = max(cls_avg_factor, 1) loss_cls = self.loss_cls( cls_scores, labels, label_weights, avg_factor=cls_avg_factor) # Compute the average number of gt boxes accross all gpus, for # normalization purposes num_total_pos = loss_cls.new_tensor([num_total_pos]) num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item() # regression L1 loss bbox_preds = bbox_preds.reshape(-1, bbox_preds.size(-1)) normalized_bbox_targets = normalize_bbox(bbox_targets, self.pc_range) isnotnan = torch.isfinite(normalized_bbox_targets).all(dim=-1) bbox_weights = bbox_weights * self.code_weights loss_bbox = self.loss_bbox( bbox_preds[isnotnan, :10], normalized_bbox_targets[isnotnan, :10], bbox_weights[isnotnan, :10], avg_factor=num_total_pos) if digit_version(torch.__version__) >= digit_version('1.8'): loss_cls = torch.nan_to_num(loss_cls) loss_bbox = torch.nan_to_num(loss_bbox) return loss_cls, loss_bbox def loss(self, gt_bboxes_list, gt_labels_list, preds_dicts, gt_bboxes_ignore=None, img_metas=None): """"Loss function. Args: gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels_list (list[Tensor]): Ground truth class indices for each image with shape (num_gts, ). preds_dicts: all_cls_scores (Tensor): Classification score of all decoder layers, has shape [nb_dec, bs, num_query, cls_out_channels]. all_bbox_preds (Tensor): Sigmoid regression outputs of all decode layers. Each is a 4D-tensor with normalized coordinate format (cx, cy, w, h) and shape [nb_dec, bs, num_query, 4]. enc_cls_scores (Tensor): Classification scores of points on encode feature map , has shape (N, h*w, num_classes). Only be passed when as_two_stage is True, otherwise is None. enc_bbox_preds (Tensor): Regression results of each points on the encode feature map, has shape (N, h*w, 4). Only be passed when as_two_stage is True, otherwise is None. gt_bboxes_ignore (list[Tensor], optional): Bounding boxes which can be ignored for each image. Default None. Returns: dict[str, Tensor]: A dictionary of loss components. """ assert gt_bboxes_ignore is None, \ f'{self.__class__.__name__} only supports ' \ f'for gt_bboxes_ignore setting to None.' all_cls_scores = preds_dicts['all_cls_scores'] all_bbox_preds = preds_dicts['all_bbox_preds'] enc_cls_scores = preds_dicts['enc_cls_scores'] enc_bbox_preds = preds_dicts['enc_bbox_preds'] num_dec_layers = len(all_cls_scores) device = gt_labels_list[0].device gt_bboxes_list = [ torch.cat((gt_bboxes.gravity_center, gt_bboxes.tensor[:, 3:]), dim=1).to(device) for gt_bboxes in gt_bboxes_list ] all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)] all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)] all_gt_bboxes_ignore_list = [ gt_bboxes_ignore for _ in range(num_dec_layers) ] losses_cls, losses_bbox = multi_apply(self.loss_single, all_cls_scores, all_bbox_preds, all_gt_bboxes_list, all_gt_labels_list, all_gt_bboxes_ignore_list) loss_dict = dict() # for one2many task if 'all_cls_scores_aux' in preds_dicts and self.one2many_gt_mul: all_cls_scores_aux = preds_dicts['all_cls_scores_aux'] all_bbox_preds_aux = preds_dicts['all_bbox_preds_aux'] gt_bboxes_list_aux = [] gt_labels_list_aux = [] # for gt_bboxes, gt_labels in zip(gt_bboxes_list, gt_labels_list): # gt_bboxes_list_aux.append( # gt_bboxes.repeat(self.one2many_gt_mul, 1)) # gt_labels_list_aux.append( # gt_labels.repeat(self.one2many_gt_mul)) # for classwise multiply for gt_bboxes, gt_labels in zip(gt_bboxes_list, gt_labels_list): gt_bboxes_aux = [] gt_labels_aux = [] for gt_bbox, gt_label in zip(gt_bboxes, gt_labels): gt_bboxes_aux += [gt_bbox] * self.one2many_gt_mul[gt_label] gt_labels_aux += [gt_label ] * self.one2many_gt_mul[gt_label] gt_bboxes_list_aux.append(torch.stack(gt_bboxes_aux)) gt_labels_list_aux.append(torch.stack(gt_labels_aux)) all_gt_bboxes_list_aux = [ gt_bboxes_list_aux for _ in range(num_dec_layers) ] all_gt_labels_list_aux = [ gt_labels_list_aux for _ in range(num_dec_layers) ] losses_cls_aux, losses_bbox_aux = multi_apply( self.loss_single, all_cls_scores_aux, all_bbox_preds_aux, all_gt_bboxes_list_aux, all_gt_labels_list_aux, all_gt_bboxes_ignore_list) loss_dict['loss_cls_aux'] = losses_cls_aux[-1] loss_dict['loss_bbox_aux'] = losses_bbox_aux[-1] num_dec_layer = 0 for loss_cls_i, loss_bbox_i in zip(losses_cls_aux[:-1], losses_bbox_aux[:-1]): loss_dict[f'd{num_dec_layer}.loss_cls_aux'] = loss_cls_i loss_dict[f'd{num_dec_layer}.loss_bbox_aux'] = loss_bbox_i num_dec_layer += 1 # loss of proposal generated from encode feature map. if enc_cls_scores is not None: binary_labels_list = [ torch.zeros_like(gt_labels_list[i]) for i in range(len(all_gt_labels_list)) ] enc_loss_cls, enc_losses_bbox = \ self.loss_single(enc_cls_scores, enc_bbox_preds, gt_bboxes_list, binary_labels_list, gt_bboxes_ignore) loss_dict['enc_loss_cls'] = enc_loss_cls loss_dict['enc_loss_bbox'] = enc_losses_bbox # loss from the last decoder layer loss_dict['loss_cls'] = losses_cls[-1] loss_dict['loss_bbox'] = losses_bbox[-1] # loss from other decoder layers num_dec_layer = 0 for loss_cls_i, loss_bbox_i in zip(losses_cls[:-1], losses_bbox[:-1]): loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i num_dec_layer += 1 return loss_dict # over-write because img_metas are needed as inputs for bbox_head. def forward_train(self, x, img_metas, gt_bboxes, gt_labels=None, gt_bboxes_ignore=None, proposal_cfg=None, **kwargs): """Forward function for training mode. Args: x (list[Tensor]): Features from backbone. img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. gt_bboxes (Tensor): Ground truth bboxes of the image, shape (num_gts, 4). gt_labels (Tensor): Ground truth labels of each box, shape (num_gts,). gt_bboxes_ignore (Tensor): Ground truth bboxes to be ignored, shape (num_ignored_gts, 4). proposal_cfg (mmcv.Config): Test / postprocessing configuration, if None, test_cfg would be used. Returns: dict[str, Tensor]: A dictionary of loss components. """ assert proposal_cfg is None, '"proposal_cfg" must be None' outs = self(x, img_metas) if gt_labels is None: loss_inputs = outs + (gt_bboxes, img_metas) else: loss_inputs = outs + (gt_bboxes, gt_labels, img_metas) losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) return losses def get_bboxes(self, preds_dicts, img_metas, rescale=False): """Generate bboxes from bbox head predictions. Args: preds_dicts (tuple[list[dict]]): Prediction results. img_metas (list[dict]): Point cloud and image's meta info. Returns: list[dict]: Decoded bbox, scores and labels after nms. """ preds_dicts = self.bbox_coder.decode(preds_dicts) num_samples = len(preds_dicts) ret_list = [] for i in range(num_samples): preds = preds_dicts[i] bboxes = preds['bboxes'] bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 5] * 0.5 code_size = bboxes.shape[-1] bboxes = img_metas[i]['box_type_3d'](bboxes, code_size) scores = preds['scores'] labels = preds['labels'] ret_list.append([bboxes, scores, labels]) return ret_list