From 682687e697ad290a928c03de05beecae8acf7e8b Mon Sep 17 00:00:00 2001 From: inter Date: Sun, 21 Sep 2025 20:19:01 +0800 Subject: [PATCH] Add File --- pcdet/models/roi_heads/mppnet_head.py | 992 ++++++++++++++++++++++++++ 1 file changed, 992 insertions(+) create mode 100644 pcdet/models/roi_heads/mppnet_head.py diff --git a/pcdet/models/roi_heads/mppnet_head.py b/pcdet/models/roi_heads/mppnet_head.py new file mode 100644 index 0000000..909b9c7 --- /dev/null +++ b/pcdet/models/roi_heads/mppnet_head.py @@ -0,0 +1,992 @@ +from typing import ValuesView +import torch.nn as nn +import torch +import numpy as np +import copy +import torch.nn.functional as F +from pcdet.ops.iou3d_nms import iou3d_nms_utils +from ...utils import common_utils, loss_utils +from .roi_head_template import RoIHeadTemplate +from ..model_utils.mppnet_utils import build_transformer, PointNet, MLP +from .target_assigner.proposal_target_layer import ProposalTargetLayer +from pcdet.ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules + + +class ProposalTargetLayerMPPNet(ProposalTargetLayer): + def __init__(self, roi_sampler_cfg): + super().__init__(roi_sampler_cfg = roi_sampler_cfg) + + def forward(self, batch_dict): + """ + Args: + batch_dict: + batch_size: + rois: (B, num_rois, 7 + C) + roi_scores: (B, num_rois) + gt_boxes: (B, N, 7 + C + 1) + roi_labels: (B, num_rois) + Returns: + batch_dict: + rois: (B, M, 7 + C) + gt_of_rois: (B, M, 7 + C) + gt_iou_of_rois: (B, M) + roi_scores: (B, M) + roi_labels: (B, M) + reg_valid_mask: (B, M) + rcnn_cls_labels: (B, M) + """ + + batch_rois, batch_gt_of_rois, batch_roi_ious, batch_roi_scores, batch_roi_labels, \ + batch_trajectory_rois,batch_valid_length = self.sample_rois_for_mppnet(batch_dict=batch_dict) + + # regression valid mask + reg_valid_mask = (batch_roi_ious > self.roi_sampler_cfg.REG_FG_THRESH).long() + + # classification label + if self.roi_sampler_cfg.CLS_SCORE_TYPE == 'cls': + batch_cls_labels = (batch_roi_ious > self.roi_sampler_cfg.CLS_FG_THRESH).long() + ignore_mask = (batch_roi_ious > self.roi_sampler_cfg.CLS_BG_THRESH) & \ + (batch_roi_ious < self.roi_sampler_cfg.CLS_FG_THRESH) + batch_cls_labels[ignore_mask > 0] = -1 + elif self.roi_sampler_cfg.CLS_SCORE_TYPE == 'roi_iou': + iou_bg_thresh = self.roi_sampler_cfg.CLS_BG_THRESH + iou_fg_thresh = self.roi_sampler_cfg.CLS_FG_THRESH + fg_mask = batch_roi_ious > iou_fg_thresh + bg_mask = batch_roi_ious < iou_bg_thresh + interval_mask = (fg_mask == 0) & (bg_mask == 0) + + batch_cls_labels = (fg_mask > 0).float() + batch_cls_labels[interval_mask] = \ + (batch_roi_ious[interval_mask] - iou_bg_thresh) / (iou_fg_thresh - iou_bg_thresh) + else: + raise NotImplementedError + + + targets_dict = {'rois': batch_rois, 'gt_of_rois': batch_gt_of_rois, + 'gt_iou_of_rois': batch_roi_ious,'roi_scores': batch_roi_scores, + 'roi_labels': batch_roi_labels,'reg_valid_mask': reg_valid_mask, + 'rcnn_cls_labels': batch_cls_labels,'trajectory_rois':batch_trajectory_rois, + 'valid_length': batch_valid_length, + } + + return targets_dict + + def sample_rois_for_mppnet(self, batch_dict): + """ + Args: + batch_dict: + batch_size: + rois: (B, num_rois, 7 + C) + roi_scores: (B, num_rois) + gt_boxes: (B, N, 7 + C + 1) + roi_labels: (B, num_rois) + Returns: + """ + cur_frame_idx = 0 + batch_size = batch_dict['batch_size'] + rois = batch_dict['trajectory_rois'][:,cur_frame_idx,:,:] + roi_scores = batch_dict['roi_scores'][:,:,cur_frame_idx] + roi_labels = batch_dict['roi_labels'] + gt_boxes = batch_dict['gt_boxes'] + + code_size = rois.shape[-1] + batch_rois = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE, code_size) + batch_gt_of_rois = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE, gt_boxes.shape[-1]) + batch_roi_ious = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE) + batch_roi_scores = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE) + batch_roi_labels = rois.new_zeros((batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE), dtype=torch.long) + + + + trajectory_rois = batch_dict['trajectory_rois'] + batch_trajectory_rois = rois.new_zeros(batch_size, trajectory_rois.shape[1],self.roi_sampler_cfg.ROI_PER_IMAGE,trajectory_rois.shape[-1]) + + valid_length = batch_dict['valid_length'] + batch_valid_length = rois.new_zeros((batch_size, batch_dict['trajectory_rois'].shape[1], self.roi_sampler_cfg.ROI_PER_IMAGE)) + + for index in range(batch_size): + + cur_trajectory_rois = trajectory_rois[index] + + cur_roi, cur_gt, cur_roi_labels, cur_roi_scores = rois[index],gt_boxes[index], roi_labels[index], roi_scores[index] + + if 'valid_length' in batch_dict.keys(): + cur_valid_length = valid_length[index] + + + + k = cur_gt.__len__() - 1 + while k > 0 and cur_gt[k].sum() == 0: + k -= 1 + + cur_gt = cur_gt[:k + 1] + cur_gt = cur_gt.new_zeros((1, cur_gt.shape[1])) if len(cur_gt) == 0 else cur_gt + + if self.roi_sampler_cfg.get('SAMPLE_ROI_BY_EACH_CLASS', False): + max_overlaps, gt_assignment = self.get_max_iou_with_same_class( + rois=cur_roi, roi_labels=cur_roi_labels, + gt_boxes=cur_gt[:, 0:7], gt_labels=cur_gt[:, -1].long() + ) + + else: + iou3d = iou3d_nms_utils.boxes_iou3d_gpu(cur_roi, cur_gt[:, 0:7]) # (M, N) + max_overlaps, gt_assignment = torch.max(iou3d, dim=1) + + sampled_inds,fg_inds, bg_inds = self.subsample_rois(max_overlaps=max_overlaps) + + batch_roi_labels[index] = cur_roi_labels[sampled_inds.long()] + + + if self.roi_sampler_cfg.get('USE_ROI_AUG',False): + + fg_rois, fg_iou3d = self.aug_roi_by_noise_torch(cur_roi[fg_inds], cur_gt[gt_assignment[fg_inds]], + max_overlaps[fg_inds], aug_times=self.roi_sampler_cfg.ROI_FG_AUG_TIMES) + bg_rois = cur_roi[bg_inds] + bg_iou3d = max_overlaps[bg_inds] + + batch_rois[index] = torch.cat([fg_rois,bg_rois],0) + batch_roi_ious[index] = torch.cat([fg_iou3d,bg_iou3d],0) + batch_gt_of_rois[index] = cur_gt[gt_assignment[sampled_inds]] + + else: + batch_rois[index] = cur_roi[sampled_inds] + batch_roi_ious[index] = max_overlaps[sampled_inds] + batch_gt_of_rois[index] = cur_gt[gt_assignment[sampled_inds]] + + + batch_roi_scores[index] = cur_roi_scores[sampled_inds] + + if 'valid_length' in batch_dict.keys(): + batch_valid_length[index] = cur_valid_length[:,sampled_inds] + + if self.roi_sampler_cfg.USE_TRAJ_AUG.ENABLED: + batch_trajectory_rois_list = [] + for idx in range(0,batch_dict['num_frames']): + if idx== cur_frame_idx: + batch_trajectory_rois_list.append(cur_trajectory_rois[cur_frame_idx:cur_frame_idx+1,sampled_inds]) + continue + fg_trajs, _ = self.aug_roi_by_noise_torch(cur_trajectory_rois[idx,fg_inds], cur_trajectory_rois[idx,fg_inds][:,:8], max_overlaps[fg_inds], \ + aug_times=self.roi_sampler_cfg.ROI_FG_AUG_TIMES,pos_thresh=self.roi_sampler_cfg.USE_TRAJ_AUG.THRESHOD) + bg_trajs = cur_trajectory_rois[idx,bg_inds] + batch_trajectory_rois_list.append(torch.cat([fg_trajs,bg_trajs],0)[None,:,:]) + batch_trajectory_rois[index] = torch.cat(batch_trajectory_rois_list,0) + else: + batch_trajectory_rois[index] = cur_trajectory_rois[:,sampled_inds] + + return batch_rois, batch_gt_of_rois, batch_roi_ious, batch_roi_scores, batch_roi_labels, batch_trajectory_rois,batch_valid_length + + def subsample_rois(self, max_overlaps): + # sample fg, easy_bg, hard_bg + fg_rois_per_image = int(np.round(self.roi_sampler_cfg.FG_RATIO * self.roi_sampler_cfg.ROI_PER_IMAGE)) + fg_thresh = min(self.roi_sampler_cfg.REG_FG_THRESH, self.roi_sampler_cfg.CLS_FG_THRESH) + + fg_inds = ((max_overlaps >= fg_thresh)).nonzero().view(-1) + easy_bg_inds = ((max_overlaps < self.roi_sampler_cfg.CLS_BG_THRESH_LO)).nonzero().view(-1) + hard_bg_inds = ((max_overlaps < self.roi_sampler_cfg.REG_FG_THRESH) & + (max_overlaps >= self.roi_sampler_cfg.CLS_BG_THRESH_LO)).nonzero().view(-1) + + fg_num_rois = fg_inds.numel() + bg_num_rois = hard_bg_inds.numel() + easy_bg_inds.numel() + + if fg_num_rois > 0 and bg_num_rois > 0: + # sampling fg + fg_rois_per_this_image = min(fg_rois_per_image, fg_num_rois) + + rand_num = torch.from_numpy(np.random.permutation(fg_num_rois)).type_as(max_overlaps).long() + fg_inds = fg_inds[rand_num[:fg_rois_per_this_image]] + + # sampling bg + bg_rois_per_this_image = self.roi_sampler_cfg.ROI_PER_IMAGE - fg_rois_per_this_image + bg_inds = self.sample_bg_inds( + hard_bg_inds, easy_bg_inds, bg_rois_per_this_image, self.roi_sampler_cfg.HARD_BG_RATIO + ) + + elif fg_num_rois > 0 and bg_num_rois == 0: + # sampling fg + rand_num = np.floor(np.random.rand(self.roi_sampler_cfg.ROI_PER_IMAGE) * fg_num_rois) + rand_num = torch.from_numpy(rand_num).type_as(max_overlaps).long() + fg_inds = fg_inds[rand_num] + bg_inds = torch.tensor([]).type_as(fg_inds) + + elif bg_num_rois > 0 and fg_num_rois == 0: + # sampling bg + bg_rois_per_this_image = self.roi_sampler_cfg.ROI_PER_IMAGE + bg_inds = self.sample_bg_inds( + hard_bg_inds, easy_bg_inds, bg_rois_per_this_image, self.roi_sampler_cfg.HARD_BG_RATIO + ) + else: + print('maxoverlaps:(min=%f, max=%f)' % (max_overlaps.min().item(), max_overlaps.max().item())) + print('ERROR: FG=%d, BG=%d' % (fg_num_rois, bg_num_rois)) + raise NotImplementedError + + sampled_inds = torch.cat((fg_inds, bg_inds), dim=0) + return sampled_inds.long(), fg_inds.long(), bg_inds.long() + + def aug_roi_by_noise_torch(self,roi_boxes3d, gt_boxes3d, iou3d_src, aug_times=10, pos_thresh=None): + iou_of_rois = torch.zeros(roi_boxes3d.shape[0]).type_as(gt_boxes3d) + if pos_thresh is None: + pos_thresh = min(self.roi_sampler_cfg.REG_FG_THRESH, self.roi_sampler_cfg.CLS_FG_THRESH) + + for k in range(roi_boxes3d.shape[0]): + temp_iou = cnt = 0 + roi_box3d = roi_boxes3d[k] + + gt_box3d = gt_boxes3d[k].view(1, gt_boxes3d.shape[-1]) + aug_box3d = roi_box3d + keep = True + while temp_iou < pos_thresh and cnt < aug_times: + if np.random.rand() <= self.roi_sampler_cfg.RATIO: + aug_box3d = roi_box3d # p=RATIO to keep the original roi box + keep = True + else: + aug_box3d = self.random_aug_box3d(roi_box3d) + keep = False + aug_box3d = aug_box3d.view((1, aug_box3d.shape[-1])) + iou3d = iou3d_nms_utils.boxes_iou3d_gpu(aug_box3d[:,:7], gt_box3d[:,:7]) + temp_iou = iou3d[0][0] + cnt += 1 + roi_boxes3d[k] = aug_box3d.view(-1) + if cnt == 0 or keep: + iou_of_rois[k] = iou3d_src[k] + else: + iou_of_rois[k] = temp_iou + return roi_boxes3d, iou_of_rois + + def random_aug_box3d(self,box3d): + """ + :param box3d: (7) [x, y, z, h, w, l, ry] + random shift, scale, orientation + """ + + if self.roi_sampler_cfg.REG_AUG_METHOD == 'single': + pos_shift = (torch.rand(3, device=box3d.device) - 0.5) # [-0.5 ~ 0.5] + hwl_scale = (torch.rand(3, device=box3d.device) - 0.5) / (0.5 / 0.15) + 1.0 # + angle_rot = (torch.rand(1, device=box3d.device) - 0.5) / (0.5 / (np.pi / 12)) # [-pi/12 ~ pi/12] + aug_box3d = torch.cat([box3d[0:3] + pos_shift, box3d[3:6] * hwl_scale, box3d[6:7] + angle_rot, box3d[7:]], dim=0) + return aug_box3d + elif self.roi_sampler_cfg.REG_AUG_METHOD == 'multiple': + # pos_range, hwl_range, angle_range, mean_iou + range_config = [[0.2, 0.1, np.pi / 12, 0.7], + [0.3, 0.15, np.pi / 12, 0.6], + [0.5, 0.15, np.pi / 9, 0.5], + [0.8, 0.15, np.pi / 6, 0.3], + [1.0, 0.15, np.pi / 3, 0.2]] + idx = torch.randint(low=0, high=len(range_config), size=(1,))[0].long() + + pos_shift = ((torch.rand(3, device=box3d.device) - 0.5) / 0.5) * range_config[idx][0] + hwl_scale = ((torch.rand(3, device=box3d.device) - 0.5) / 0.5) * range_config[idx][1] + 1.0 + angle_rot = ((torch.rand(1, device=box3d.device) - 0.5) / 0.5) * range_config[idx][2] + + aug_box3d = torch.cat([box3d[0:3] + pos_shift, box3d[3:6] * hwl_scale, box3d[6:7] + angle_rot], dim=0) + return aug_box3d + elif self.roi_sampler_cfg.REG_AUG_METHOD == 'normal': + x_shift = np.random.normal(loc=0, scale=0.3) + y_shift = np.random.normal(loc=0, scale=0.2) + z_shift = np.random.normal(loc=0, scale=0.3) + h_shift = np.random.normal(loc=0, scale=0.25) + w_shift = np.random.normal(loc=0, scale=0.15) + l_shift = np.random.normal(loc=0, scale=0.5) + ry_shift = ((torch.rand() - 0.5) / 0.5) * np.pi / 12 + + aug_box3d = np.array([box3d[0] + x_shift, box3d[1] + y_shift, box3d[2] + z_shift, box3d[3] + h_shift, + box3d[4] + w_shift, box3d[5] + l_shift, box3d[6] + ry_shift], dtype=np.float32) + aug_box3d = torch.from_numpy(aug_box3d).type_as(box3d) + return aug_box3d + else: + raise NotImplementedError + +class MPPNetHead(RoIHeadTemplate): + def __init__(self,model_cfg, num_class=1,**kwargs): + super().__init__(num_class=num_class, model_cfg=model_cfg) + self.model_cfg = model_cfg + self.proposal_target_layer = ProposalTargetLayerMPPNet(roi_sampler_cfg=self.model_cfg.TARGET_CONFIG) + self.use_time_stamp = self.model_cfg.get('USE_TIMESTAMP',None) + self.num_lidar_points = self.model_cfg.Transformer.num_lidar_points + self.avg_stage1_score = self.model_cfg.get('AVG_STAGE1_SCORE', None) + + self.nhead = model_cfg.Transformer.nheads + self.num_enc_layer = model_cfg.Transformer.enc_layers + hidden_dim = model_cfg.TRANS_INPUT + self.hidden_dim = model_cfg.TRANS_INPUT + self.num_groups = model_cfg.Transformer.num_groups + + self.grid_size = model_cfg.ROI_GRID_POOL.GRID_SIZE + self.num_proxy_points = model_cfg.Transformer.num_proxy_points + self.seqboxembed = PointNet(8,model_cfg=self.model_cfg) + self.jointembed = MLP(self.hidden_dim*(self.num_groups+1), model_cfg.Transformer.hidden_dim, self.box_coder.code_size * self.num_class, 4) + + + num_radius = len(self.model_cfg.ROI_GRID_POOL.POOL_RADIUS) + self.up_dimension_geometry = MLP(input_dim = 29, hidden_dim = 64, output_dim =hidden_dim//num_radius, num_layers = 3) + self.up_dimension_motion = MLP(input_dim = 30, hidden_dim = 64, output_dim = hidden_dim, num_layers = 3) + + self.transformer = build_transformer(model_cfg.Transformer) + + self.roi_grid_pool_layer = pointnet2_stack_modules.StackSAModuleMSG( + radii=self.model_cfg.ROI_GRID_POOL.POOL_RADIUS, + nsamples=self.model_cfg.ROI_GRID_POOL.NSAMPLE, + mlps=self.model_cfg.ROI_GRID_POOL.MLPS, + use_xyz=True, + pool_method=self.model_cfg.ROI_GRID_POOL.POOL_METHOD, + ) + + self.class_embed = nn.ModuleList() + self.class_embed.append(nn.Linear(model_cfg.Transformer.hidden_dim, 1)) + + self.bbox_embed = nn.ModuleList() + for _ in range(self.num_groups): + self.bbox_embed.append(MLP(model_cfg.Transformer.hidden_dim, model_cfg.Transformer.hidden_dim, self.box_coder.code_size * self.num_class, 4)) + + if self.model_cfg.Transformer.use_grid_pos.enabled: + if self.model_cfg.Transformer.use_grid_pos.init_type == 'index': + self.grid_index = torch.cat([i.reshape(-1,1)for i in torch.meshgrid(torch.arange(self.grid_size), torch.arange(self.grid_size), torch.arange(self.grid_size))],1).float().cuda() + self.grid_pos_embeded = MLP(input_dim = 3, hidden_dim = 256, output_dim = hidden_dim, num_layers = 2) + else: + self.pos = nn.Parameter(torch.zeros(1, self.num_grid_points, 256)) + + 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.bbox_embed.layers[-1].weight, mean=0, std=0.001) + + def get_corner_points_of_roi(self, rois): + rois = rois.view(-1, rois.shape[-1]) + batch_size_rcnn = rois.shape[0] + + local_roi_grid_points = self.get_corner_points(rois, batch_size_rcnn) + local_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 = local_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() + dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float() + + 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) + return roi_grid_points + + @staticmethod + def get_corner_points(rois, batch_size_rcnn): + faked_features = rois.new_ones((2, 2, 2)) + + dense_idx = faked_features.nonzero() + dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float() + + local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6] + roi_grid_points = dense_idx * local_roi_size.unsqueeze(dim=1) \ + - (local_roi_size.unsqueeze(dim=1) / 2) + return roi_grid_points + + def roi_grid_pool(self, batch_size, rois, point_coords, point_features,batch_dict=None,batch_cnt=None): + + num_frames = batch_dict['num_frames'] + num_rois = rois.shape[2]*rois.shape[1] + + global_roi_proxy_points, local_roi_proxy_points = self.get_proxy_points_of_roi( + rois.permute(0,2,1,3).contiguous(), grid_size=self.grid_size + ) + + global_roi_proxy_points = global_roi_proxy_points.view(batch_size, -1, 3) + + + point_coords = point_coords.view(point_coords.shape[0]*num_frames,point_coords.shape[1]//num_frames,point_coords.shape[-1]) + xyz = point_coords[:, :, 0:3].view(-1,3) + + + num_points = point_coords.shape[1] + num_proxy_points = self.num_proxy_points + + if batch_cnt is None: + xyz_batch_cnt = torch.tensor([num_points]*num_rois*batch_size).cuda().int() + else: + xyz_batch_cnt = torch.tensor(batch_cnt).cuda().int() + + new_xyz_batch_cnt = torch.tensor([num_proxy_points]*num_rois*batch_size).cuda().int() + new_xyz = global_roi_proxy_points.view(-1, 3) + + _, pooled_features = self.roi_grid_pool_layer( + xyz=xyz.contiguous(), + xyz_batch_cnt=xyz_batch_cnt, + new_xyz=new_xyz, + new_xyz_batch_cnt=new_xyz_batch_cnt, + features=point_features.view(-1,point_features.shape[-1]).contiguous(), + ) + + features = pooled_features.view( + point_features.shape[0], num_frames*self.num_proxy_points, + pooled_features.shape[-1]).contiguous() + + return features,global_roi_proxy_points.view(batch_size*rois.shape[2], num_frames*num_proxy_points,3).contiguous() + + def get_proxy_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) + local_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 = local_roi_grid_points + global_center.unsqueeze(dim=1) + return global_roi_grid_points, local_roi_grid_points + + def spherical_coordinate(self, src, diag_dist): + assert (src.shape[-1] == 27) + device = src.device + indices_x = torch.LongTensor([0,3,6,9,12,15,18,21,24]).to(device) # + indices_y = torch.LongTensor([1,4,7,10,13,16,19,22,25]).to(device) # + indices_z = torch.LongTensor([2,5,8,11,14,17,20,23,26]).to(device) + src_x = torch.index_select(src, -1, indices_x) + src_y = torch.index_select(src, -1, indices_y) + src_z = torch.index_select(src, -1, indices_z) + dis = (src_x ** 2 + src_y ** 2 + src_z ** 2) ** 0.5 + phi = torch.atan(src_y / (src_x + 1e-5)) + the = torch.acos(src_z / (dis + 1e-5)) + dis = dis / (diag_dist + 1e-5) + src = torch.cat([dis, phi, the], dim = -1) + return src + + def crop_current_frame_points(self, src, batch_size,trajectory_rois,num_rois,batch_dict): + + for bs_idx in range(batch_size): + cur_batch_boxes = trajectory_rois[bs_idx,0,:,:7].view(-1,7) + cur_radiis = torch.sqrt((cur_batch_boxes[:,3]/2) ** 2 + (cur_batch_boxes[:,4]/2) ** 2) * 1.1 + cur_points = batch_dict['points'][(batch_dict['points'][:, 0] == bs_idx)][:,1:] + dis = torch.norm((cur_points[:,:2].unsqueeze(0) - cur_batch_boxes[:,:2].unsqueeze(1).repeat(1,cur_points.shape[0],1)), dim = 2) + point_mask = (dis <= cur_radiis.unsqueeze(-1)) + + + sampled_idx = torch.topk(point_mask.float(),128)[1] + sampled_idx_buffer = sampled_idx[:, 0:1].repeat(1, 128) + roi_idx = torch.arange(num_rois)[:, None].repeat(1, 128) + sampled_mask = point_mask[roi_idx, sampled_idx] + sampled_idx_buffer[sampled_mask] = sampled_idx[sampled_mask] + + src[bs_idx] = cur_points[sampled_idx_buffer][:,:,:5] + empty_flag = sampled_mask.sum(-1)==0 + src[bs_idx,empty_flag] = 0 + + src = src.repeat([1,1,trajectory_rois.shape[1],1]) + + return src + + def crop_previous_frame_points(self,src,batch_size,trajectory_rois,num_rois,valid_length,batch_dict): + for bs_idx in range(batch_size): + + cur_points = batch_dict['points'][(batch_dict['points'][:, 0] == bs_idx)][:,1:] + + + for idx in range(1,trajectory_rois.shape[1]): + + time_mask = (cur_points[:,-1] - idx*0.1).abs() < 1e-3 + cur_time_points = cur_points[time_mask] + cur_batch_boxes = trajectory_rois[bs_idx,idx,:,:7].view(-1,7) + + cur_radiis = torch.sqrt((cur_batch_boxes[:,3]/2) ** 2 + (cur_batch_boxes[:,4]/2) ** 2) * 1.1 + if not self.training and cur_batch_boxes.shape[0] > 32: + length_iter= cur_batch_boxes.shape[0]//32 + dis_list = [] + for i in range(length_iter+1): + dis = torch.norm((cur_time_points[:,:2].unsqueeze(0) - \ + cur_batch_boxes[32*i:32*(i+1),:2].unsqueeze(1).repeat(1,cur_time_points.shape[0],1)), dim = 2) + dis_list.append(dis) + dis = torch.cat(dis_list,0) + else: + dis = torch.norm((cur_time_points[:,:2].unsqueeze(0) - \ + cur_batch_boxes[:,:2].unsqueeze(1).repeat(1,cur_time_points.shape[0],1)), dim = 2) + + point_mask = (dis <= cur_radiis.unsqueeze(-1)).view(trajectory_rois.shape[2],-1) + + for roi_box_idx in range(0, num_rois): + + if not valid_length[bs_idx,idx,roi_box_idx]: + continue + + cur_roi_points = cur_time_points[point_mask[roi_box_idx]] + + if cur_roi_points.shape[0] > self.num_lidar_points: + np.random.seed(0) + choice = np.random.choice(cur_roi_points.shape[0], self.num_lidar_points, replace=True) + cur_roi_points_sample = cur_roi_points[choice] + + elif cur_roi_points.shape[0] == 0: + cur_roi_points_sample = cur_roi_points.new_zeros(self.num_lidar_points, 6) + + else: + empty_num = self.num_lidar_points - cur_roi_points.shape[0] + add_zeros = cur_roi_points.new_zeros(empty_num, 6) + add_zeros = cur_roi_points[0].repeat(empty_num, 1) + cur_roi_points_sample = torch.cat([cur_roi_points, add_zeros], dim = 0) + + if not self.use_time_stamp: + cur_roi_points_sample = cur_roi_points_sample[:,:-1] + + src[bs_idx, roi_box_idx, self.num_lidar_points*idx:self.num_lidar_points*(idx+1), :] = cur_roi_points_sample + + + return src + + + def get_proposal_aware_geometry_feature(self,src, batch_size,trajectory_rois,num_rois,batch_dict): + proposal_aware_feat_list = [] + for i in range(trajectory_rois.shape[1]): + + corner_points, _ = self.get_corner_points_of_roi(trajectory_rois[:,i,:,:].contiguous()) + + corner_points = corner_points.view(batch_size, num_rois, -1, corner_points.shape[-1]) + corner_points = corner_points.view(batch_size * num_rois, -1) + trajectory_roi_center = trajectory_rois[:,i,:,:].contiguous().reshape(batch_size * num_rois, -1)[:,:3] + corner_add_center_points = torch.cat([corner_points, trajectory_roi_center], dim = -1) + proposal_aware_feat = src[:,i*self.num_lidar_points:(i+1)*self.num_lidar_points,:3].repeat(1,1,9) - \ + corner_add_center_points.unsqueeze(1).repeat(1,self.num_lidar_points,1) + + lwh = trajectory_rois[:,i,:,:].reshape(batch_size * num_rois, -1)[:,3:6].unsqueeze(1).repeat(1,proposal_aware_feat.shape[1],1) + diag_dist = (lwh[:,:,0]**2 + lwh[:,:,1]**2 + lwh[:,:,2]**2) ** 0.5 + proposal_aware_feat = self.spherical_coordinate(proposal_aware_feat, diag_dist = diag_dist.unsqueeze(-1)) + proposal_aware_feat_list.append(proposal_aware_feat) + + proposal_aware_feat = torch.cat(proposal_aware_feat_list,dim=1) + proposal_aware_feat = torch.cat([proposal_aware_feat, src[:,:,3:]], dim = -1) + src_gemoetry = self.up_dimension_geometry(proposal_aware_feat) + proxy_point_geometry, proxy_points = self.roi_grid_pool(batch_size,trajectory_rois,src,src_gemoetry,batch_dict,batch_cnt=None) + return proxy_point_geometry,proxy_points + + + + def get_proposal_aware_motion_feature(self,proxy_point,batch_size,trajectory_rois,num_rois,batch_dict): + + + time_stamp = torch.ones([proxy_point.shape[0],proxy_point.shape[1],1]).cuda() + padding_zero = torch.zeros([proxy_point.shape[0],proxy_point.shape[1],2]).cuda() + proxy_point_time_padding = torch.cat([padding_zero,time_stamp],-1) + + num_frames = trajectory_rois.shape[1] + + for i in range(num_frames): + proxy_point_time_padding[:,i*self.num_proxy_points:(i+1)*self.num_proxy_points,-1] = i*0.1 + + + corner_points, _ = self.get_corner_points_of_roi(trajectory_rois[:,0,:,:].contiguous()) + corner_points = corner_points.view(batch_size, num_rois, -1, corner_points.shape[-1]) + corner_points = corner_points.view(batch_size * num_rois, -1) + trajectory_roi_center = trajectory_rois[:,0,:,:].reshape(batch_size * num_rois, -1)[:,:3] + corner_add_center_points = torch.cat([corner_points, trajectory_roi_center], dim = -1) + + proposal_aware_feat = proxy_point[:,:,:3].repeat(1,1,9) - corner_add_center_points.unsqueeze(1) + + lwh = trajectory_rois[:,0,:,:].reshape(batch_size * num_rois, -1)[:,3:6].unsqueeze(1).repeat(1,proxy_point.shape[1],1) + diag_dist = (lwh[:,:,0]**2 + lwh[:,:,1]**2 + lwh[:,:,2]**2) ** 0.5 + proposal_aware_feat = self.spherical_coordinate(proposal_aware_feat, diag_dist = diag_dist.unsqueeze(-1)) + + + proposal_aware_feat = torch.cat([proposal_aware_feat,proxy_point_time_padding],-1) + proxy_point_motion_feat = self.up_dimension_motion(proposal_aware_feat) + + return proxy_point_motion_feat + + def trajectories_auxiliary_branch(self,trajectory_rois): + + time_stamp = torch.ones([trajectory_rois.shape[0],trajectory_rois.shape[1],trajectory_rois.shape[2],1]).cuda() + for i in range(time_stamp.shape[1]): + time_stamp[:,i,:] = i*0.1 + + box_seq = torch.cat([trajectory_rois[:,:,:,:7],time_stamp],-1) + + box_seq[:, :, :,0:3] = box_seq[:, :, :,0:3] - box_seq[:, 0:1, :, 0:3] + + roi_ry = box_seq[:,:,:,6] % (2 * np.pi) + roi_ry_t0 = roi_ry[:,0] + roi_ry_t0 = roi_ry_t0.repeat(1,box_seq.shape[1]) + + + box_seq = common_utils.rotate_points_along_z( + points=box_seq.view(-1, 1, box_seq.shape[-1]), angle=-roi_ry_t0.view(-1) + ).view(box_seq.shape[0],box_seq.shape[1], -1, box_seq.shape[-1]) + + box_seq[:, :, :, 6] = 0 + + batch_rcnn = box_seq.shape[0]*box_seq.shape[2] + + box_reg, box_feat, _ = self.seqboxembed(box_seq.permute(0,2,3,1).contiguous().view(batch_rcnn,box_seq.shape[-1],box_seq.shape[1])) + + return box_reg, box_feat + + def generate_trajectory(self,cur_batch_boxes,proposals_list,batch_dict): + + trajectory_rois = cur_batch_boxes[:,None,:,:].repeat(1,batch_dict['rois'].shape[-2],1,1) + trajectory_rois[:,0,:,:]= cur_batch_boxes + valid_length = torch.zeros([batch_dict['batch_size'],batch_dict['rois'].shape[-2],trajectory_rois.shape[2]]) + valid_length[:,0] = 1 + num_frames = batch_dict['rois'].shape[-2] + for i in range(1,num_frames): + frame = torch.zeros_like(cur_batch_boxes) + frame[:,:,0:2] = trajectory_rois[:,i-1,:,0:2] + trajectory_rois[:,i-1,:,7:9] + frame[:,:,2:] = trajectory_rois[:,i-1,:,2:] + + for bs_idx in range( batch_dict['batch_size']): + iou3d = iou3d_nms_utils.boxes_iou3d_gpu(frame[bs_idx,:,:7], proposals_list[bs_idx,i,:,:7]) + max_overlaps, traj_assignment = torch.max(iou3d, dim=1) + + fg_inds = ((max_overlaps >= 0.5)).nonzero().view(-1) + + valid_length[bs_idx,i,fg_inds] = 1 + + trajectory_rois[bs_idx,i,fg_inds,:] = proposals_list[bs_idx,i,traj_assignment[fg_inds]] + + batch_dict['valid_length'] = valid_length + + return trajectory_rois,valid_length + + def forward(self, batch_dict): + """ + :param input_data: input dict + :return: + """ + + batch_dict['rois'] = batch_dict['proposals_list'].permute(0,2,1,3) + num_rois = batch_dict['rois'].shape[1] + batch_dict['num_frames'] = batch_dict['rois'].shape[2] + batch_dict['roi_scores'] = batch_dict['roi_scores'].permute(0,2,1) + batch_dict['roi_labels'] = batch_dict['roi_labels'][:,0,:].long() + proposals_list = batch_dict['proposals_list'] + batch_size = batch_dict['batch_size'] + cur_batch_boxes = copy.deepcopy(batch_dict['rois'].detach())[:,:,0] + batch_dict['cur_frame_idx'] = 0 + + trajectory_rois,valid_length = self.generate_trajectory(cur_batch_boxes,proposals_list,batch_dict) + + batch_dict['traj_memory'] = trajectory_rois + batch_dict['has_class_labels'] = True + batch_dict['trajectory_rois'] = trajectory_rois + + if self.training: + targets_dict = self.assign_targets(batch_dict) + batch_dict['rois'] = targets_dict['rois'] + batch_dict['roi_scores'] = targets_dict['roi_scores'] + batch_dict['roi_labels'] = targets_dict['roi_labels'] + targets_dict['trajectory_rois'][:,batch_dict['cur_frame_idx'],:,:] = batch_dict['rois'] + trajectory_rois = targets_dict['trajectory_rois'] + valid_length = targets_dict['valid_length'] + empty_mask = batch_dict['rois'][:,:,:6].sum(-1)==0 + + else: + empty_mask = batch_dict['rois'][:,:,0,:6].sum(-1)==0 + batch_dict['valid_traj_mask'] = ~empty_mask + + rois = batch_dict['rois'] + num_rois = batch_dict['rois'].shape[1] + num_sample = self.num_lidar_points + src = rois.new_zeros(batch_size, num_rois, num_sample, 5) + + src = self.crop_current_frame_points(src, batch_size, trajectory_rois, num_rois,batch_dict) + + src = self.crop_previous_frame_points(src, batch_size,trajectory_rois, num_rois,valid_length,batch_dict) + + src = src.view(batch_size * num_rois, -1, src.shape[-1]) + + src_geometry_feature,proxy_points = self.get_proposal_aware_geometry_feature(src,batch_size,trajectory_rois,num_rois,batch_dict) + + src_motion_feature = self.get_proposal_aware_motion_feature(proxy_points,batch_size,trajectory_rois,num_rois,batch_dict) + + src = src_geometry_feature + src_motion_feature + + box_reg, feat_box = self.trajectories_auxiliary_branch(trajectory_rois) + + if self.model_cfg.get('USE_TRAJ_EMPTY_MASK',None): + src[empty_mask.view(-1)] = 0 + + if self.model_cfg.Transformer.use_grid_pos.init_type == 'index': + pos = self.grid_pos_embeded(self.grid_index.cuda())[None,:,:] + pos = torch.cat([torch.zeros(1,1,self.hidden_dim).cuda(),pos],1) + else: + pos=None + + hs, tokens = self.transformer(src,pos=pos) + point_cls_list = [] + point_reg_list = [] + + for i in range(self.num_enc_layer): + point_cls_list.append(self.class_embed[0](tokens[i][0])) + + for i in range(hs.shape[0]): + for j in range(self.num_enc_layer): + point_reg_list.append(self.bbox_embed[i](tokens[j][i])) + + point_cls = torch.cat(point_cls_list,0) + + point_reg = torch.cat(point_reg_list,0) + hs = hs.permute(1,0,2).reshape(hs.shape[1],-1) + + joint_reg = self.jointembed(torch.cat([hs,feat_box],-1)) + + rcnn_cls = point_cls + rcnn_reg = joint_reg + + if not self.training: + batch_dict['rois'] = batch_dict['rois'][:,:,0].contiguous() + rcnn_cls = rcnn_cls[-rcnn_cls.shape[0]//self.num_enc_layer:] + 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_box_preds'] = batch_box_preds + + batch_dict['cls_preds_normalized'] = False + if self.avg_stage1_score: + stage1_score = batch_dict['roi_scores'][:,:,:1] + batch_cls_preds = F.sigmoid(batch_cls_preds) + if self.model_cfg.get('IOU_WEIGHT', None): + batch_box_preds_list = [] + roi_labels_list = [] + batch_cls_preds_list = [] + for bs_idx in range(batch_size): + car_mask = batch_dict['roi_labels'][bs_idx] ==1 + batch_cls_preds_car = batch_cls_preds[bs_idx].pow(self.model_cfg.IOU_WEIGHT[0])* \ + stage1_score[bs_idx].pow(1-self.model_cfg.IOU_WEIGHT[0]) + batch_cls_preds_car = batch_cls_preds_car[car_mask][None] + batch_cls_preds_pedcyc = batch_cls_preds[bs_idx].pow(self.model_cfg.IOU_WEIGHT[1])* \ + stage1_score[bs_idx].pow(1-self.model_cfg.IOU_WEIGHT[1]) + batch_cls_preds_pedcyc = batch_cls_preds_pedcyc[~car_mask][None] + cls_preds = torch.cat([batch_cls_preds_car,batch_cls_preds_pedcyc],1) + box_preds = torch.cat([batch_dict['batch_box_preds'][bs_idx][car_mask], + batch_dict['batch_box_preds'][bs_idx][~car_mask]],0)[None] + roi_labels = torch.cat([batch_dict['roi_labels'][bs_idx][car_mask], + batch_dict['roi_labels'][bs_idx][~car_mask]],0)[None] + batch_box_preds_list.append(box_preds) + roi_labels_list.append(roi_labels) + batch_cls_preds_list.append(cls_preds) + batch_dict['batch_box_preds'] = torch.cat(batch_box_preds_list,0) + batch_dict['roi_labels'] = torch.cat(roi_labels_list,0) + batch_cls_preds = torch.cat(batch_cls_preds_list,0) + + else: + batch_cls_preds = torch.sqrt(batch_cls_preds*stage1_score) + batch_dict['cls_preds_normalized'] = True + + batch_dict['batch_cls_preds'] = batch_cls_preds + + + else: + targets_dict['batch_size'] = batch_size + targets_dict['rcnn_cls'] = rcnn_cls + targets_dict['rcnn_reg'] = rcnn_reg + targets_dict['box_reg'] = box_reg + targets_dict['point_reg'] = point_reg + targets_dict['point_cls'] = point_cls + self.forward_ret_dict = targets_dict + + return batch_dict + + def get_loss(self, tb_dict=None): + tb_dict = {} if tb_dict is None else tb_dict + rcnn_loss = 0 + rcnn_loss_cls, cls_tb_dict = self.get_box_cls_layer_loss(self.forward_ret_dict) + rcnn_loss += rcnn_loss_cls + tb_dict.update(cls_tb_dict) + + rcnn_loss_reg, reg_tb_dict = self.get_box_reg_layer_loss(self.forward_ret_dict) + rcnn_loss += rcnn_loss_reg + tb_dict.update(reg_tb_dict) + tb_dict['rcnn_loss'] = rcnn_loss.item() + return rcnn_loss, tb_dict + + def get_box_reg_layer_loss(self, forward_ret_dict): + loss_cfgs = self.model_cfg.LOSS_CONFIG + code_size = self.box_coder.code_size + reg_valid_mask = forward_ret_dict['reg_valid_mask'].view(-1) + batch_size = forward_ret_dict['batch_size'] + + gt_boxes3d_ct = forward_ret_dict['gt_of_rois'][..., 0:code_size] + gt_of_rois_src = forward_ret_dict['gt_of_rois_src'][..., 0:code_size].view(-1, code_size) + + rcnn_reg = forward_ret_dict['rcnn_reg'] + + roi_boxes3d = forward_ret_dict['rois'] + + rcnn_batch_size = gt_boxes3d_ct.view(-1, code_size).shape[0] + + fg_mask = (reg_valid_mask > 0) + fg_sum = fg_mask.long().sum().item() + + tb_dict = {} + + if loss_cfgs.REG_LOSS == 'smooth-l1': + + rois_anchor = roi_boxes3d.clone().detach()[:,:,:7].contiguous().view(-1, code_size) + rois_anchor[:, 0:3] = 0 + rois_anchor[:, 6] = 0 + reg_targets = self.box_coder.encode_torch( + gt_boxes3d_ct.view(rcnn_batch_size, code_size), rois_anchor + ) + rcnn_loss_reg = self.reg_loss_func( + rcnn_reg.view(rcnn_batch_size, -1).unsqueeze(dim=0), + reg_targets.unsqueeze(dim=0), + ) # [B, M, 7] + rcnn_loss_reg = (rcnn_loss_reg.view(rcnn_batch_size, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1) + rcnn_loss_reg = rcnn_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']*loss_cfgs.LOSS_WEIGHTS['traj_reg_weight'][0] + + tb_dict['rcnn_loss_reg'] = rcnn_loss_reg.item() + + if self.model_cfg.USE_AUX_LOSS: + point_reg = forward_ret_dict['point_reg'] + + groups = point_reg.shape[0]//reg_targets.shape[0] + if groups != 1 : + point_loss_regs = 0 + slice = reg_targets.shape[0] + for i in range(groups): + point_loss_reg = self.reg_loss_func( + point_reg[i*slice:(i+1)*slice].view(slice, -1).unsqueeze(dim=0),reg_targets.unsqueeze(dim=0),) + point_loss_reg = (point_loss_reg.view(slice, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1) + point_loss_reg = point_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']*loss_cfgs.LOSS_WEIGHTS['traj_reg_weight'][2] + + point_loss_regs += point_loss_reg + point_loss_regs = point_loss_regs / groups + tb_dict['point_loss_reg'] = point_loss_regs.item() + rcnn_loss_reg += point_loss_regs + + else: + point_loss_reg = self.reg_loss_func(point_reg.view(rcnn_batch_size, -1).unsqueeze(dim=0),reg_targets.unsqueeze(dim=0),) + point_loss_reg = (point_loss_reg.view(rcnn_batch_size, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1) + point_loss_reg = point_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']*loss_cfgs.LOSS_WEIGHTS['traj_reg_weight'][2] + tb_dict['point_loss_reg'] = point_loss_reg.item() + rcnn_loss_reg += point_loss_reg + + seqbox_reg = forward_ret_dict['box_reg'] + seqbox_loss_reg = self.reg_loss_func(seqbox_reg.view(rcnn_batch_size, -1).unsqueeze(dim=0),reg_targets.unsqueeze(dim=0),) + seqbox_loss_reg = (seqbox_loss_reg.view(rcnn_batch_size, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1) + seqbox_loss_reg = seqbox_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']*loss_cfgs.LOSS_WEIGHTS['traj_reg_weight'][1] + tb_dict['seqbox_loss_reg'] = seqbox_loss_reg.item() + rcnn_loss_reg += seqbox_loss_reg + + if loss_cfgs.CORNER_LOSS_REGULARIZATION and fg_sum > 0: + + fg_rcnn_reg = rcnn_reg.view(rcnn_batch_size, -1)[fg_mask] + fg_roi_boxes3d = roi_boxes3d[:,:,:7].contiguous().view(-1, code_size)[fg_mask] + + fg_roi_boxes3d = fg_roi_boxes3d.view(1, -1, code_size) + batch_anchors = fg_roi_boxes3d.clone().detach() + roi_ry = fg_roi_boxes3d[:, :, 6].view(-1) + roi_xyz = fg_roi_boxes3d[:, :, 0:3].view(-1, 3) + batch_anchors[:, :, 0:3] = 0 + rcnn_boxes3d = self.box_coder.decode_torch( + fg_rcnn_reg.view(batch_anchors.shape[0], -1, code_size), batch_anchors + ).view(-1, code_size) + + rcnn_boxes3d = common_utils.rotate_points_along_z( + rcnn_boxes3d.unsqueeze(dim=1), roi_ry + ).squeeze(dim=1) + rcnn_boxes3d[:, 0:3] += roi_xyz + + corner_loss_func = loss_utils.get_corner_loss_lidar + + loss_corner = corner_loss_func( + rcnn_boxes3d[:, 0:7], + gt_of_rois_src[fg_mask][:, 0:7]) + + loss_corner = loss_corner.mean() + loss_corner = loss_corner * loss_cfgs.LOSS_WEIGHTS['rcnn_corner_weight'] + + rcnn_loss_reg += loss_corner + tb_dict['rcnn_loss_corner'] = loss_corner.item() + + else: + raise NotImplementedError + + return rcnn_loss_reg, tb_dict + + def get_box_cls_layer_loss(self, forward_ret_dict): + loss_cfgs = self.model_cfg.LOSS_CONFIG + rcnn_cls = forward_ret_dict['rcnn_cls'] + rcnn_cls_labels = forward_ret_dict['rcnn_cls_labels'].view(-1) + + if loss_cfgs.CLS_LOSS == 'BinaryCrossEntropy': + + rcnn_cls_flat = rcnn_cls.view(-1) + + groups = rcnn_cls_flat.shape[0] // rcnn_cls_labels.shape[0] + if groups != 1: + rcnn_loss_cls = 0 + slice = rcnn_cls_labels.shape[0] + for i in range(groups): + batch_loss_cls = F.binary_cross_entropy(torch.sigmoid(rcnn_cls_flat[i*slice:(i+1)*slice]), + rcnn_cls_labels.float(), reduction='none') + + cls_valid_mask = (rcnn_cls_labels >= 0).float() + rcnn_loss_cls = rcnn_loss_cls + (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0) + + rcnn_loss_cls = rcnn_loss_cls / groups + + else: + + batch_loss_cls = F.binary_cross_entropy(torch.sigmoid(rcnn_cls_flat), rcnn_cls_labels.float(), reduction='none') + cls_valid_mask = (rcnn_cls_labels >= 0).float() + rcnn_loss_cls = (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0) + + + elif loss_cfgs.CLS_LOSS == 'CrossEntropy': + batch_loss_cls = F.cross_entropy(rcnn_cls, rcnn_cls_labels, reduction='none', ignore_index=-1) + cls_valid_mask = (rcnn_cls_labels >= 0).float() + rcnn_loss_cls = (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0) + + else: + raise NotImplementedError + + rcnn_loss_cls = rcnn_loss_cls * loss_cfgs.LOSS_WEIGHTS['rcnn_cls_weight'] + + tb_dict = {'rcnn_loss_cls': rcnn_loss_cls.item()} + return rcnn_loss_cls, tb_dict + + + def generate_predicted_boxes(self, batch_size, rois, cls_preds=None, box_preds=None): + """ + Args: + batch_size: + rois: (B, N, 7) + cls_preds: (BN, num_class) + box_preds: (BN, code_size) + Returns: + """ + code_size = self.box_coder.code_size + if cls_preds is not None: + batch_cls_preds = cls_preds.view(batch_size, -1, cls_preds.shape[-1]) + else: + batch_cls_preds = None + batch_box_preds = box_preds.view(batch_size, -1, code_size) + + roi_ry = rois[:, :, 6].view(-1) + roi_xyz = rois[:, :, 0:3].view(-1, 3) + local_rois = rois.clone().detach() + local_rois[:, :, 0:3] = 0 + + batch_box_preds = self.box_coder.decode_torch(batch_box_preds, local_rois).view(-1, code_size) + + batch_box_preds = common_utils.rotate_points_along_z( + batch_box_preds.unsqueeze(dim=1), roi_ry + ).squeeze(dim=1) + + batch_box_preds[:, 0:3] += roi_xyz + batch_box_preds = batch_box_preds.view(batch_size, -1, code_size) + batch_box_preds = torch.cat([batch_box_preds,rois[:,:,7:]],-1) + return batch_cls_preds, batch_box_preds