uniad部署.zipvsfdsfrr

_base_ = ["../_base_/datasets/nus-3d.py", "../_base_/default_runtime.py"] # Update-2023-06-12: # [Enhance] Update some freezing args of UniAD plugin = True plugin_dir = "projects/mmdet3d_plugin/" # If point cloud range is changed, the models should also change their point # cloud range accordingly point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0] voxel_size = [0.2, 0.2, 8] patch_size = [102.4, 102.4] img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) # For nuScenes we usually do 10-class detection class_names = [ "car", "truck", "construction_vehicle", "bus", "trailer", "barrier", "motorcycle", "bicycle", "pedestrian", "traffic_cone", ] vehicle_id_list = [0, 1, 2, 3, 4, 6, 7] group_id_list = [[0,1,2,3,4], [6,7], [8], [5,9]] input_modality = dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=True ) _dim_ = 256 _pos_dim_ = _dim_ // 2 _ffn_dim_ = _dim_ * 2 _num_levels_ = 4 # bev_h_ = 200 # bev_w_ = 200 bev_h_ = 40 bev_w_ = 40 _feed_dim_ = _ffn_dim_ _dim_half_ = _pos_dim_ canvas_size = (bev_h_, bev_w_) queue_length = 3 # each sequence contains `queue_length` frames. ### traj prediction args ### predict_steps = 12 predict_modes = 6 fut_steps = 4 past_steps = 4 use_nonlinear_optimizer = True ## occflow setting occ_n_future = 4 occ_n_future_plan = 6 occ_n_future_max = max([occ_n_future, occ_n_future_plan]) ### planning ### planning_steps = 6 use_col_optim = True # there exists multiple interpretations of the planning metric, where it differs between uniad and stp3/vad # uniad: computed at a particular time (e.g., L2 distance between the predicted and ground truth future trajectory at time 3.0s) # stp3: computed as the average up to a particular time (e.g., average L2 distance between the predicted and ground truth future trajectory up to 3.0s) planning_evaluation_strategy = "uniad" # uniad or stp3 ### Occ args ### # occflow_grid_conf = { # 'xbound': [-50.0, 50.0, 0.5], # 'ybound': [-50.0, 50.0, 0.5], # 'zbound': [-10.0, 10.0, 20.0], # } occflow_grid_conf = { 'xbound': [-10, 10, 0.5], 'ybound': [-10, 10, 0.5], 'zbound': [-10.0, 10.0, 20.0], } # Other settings train_gt_iou_threshold=0.3 model = dict( type="UniAD", gt_iou_threshold=train_gt_iou_threshold, queue_length=queue_length, use_grid_mask=True, video_test_mode=True, # num_query=900, num_query=100, num_classes=10, vehicle_id_list=vehicle_id_list, pc_range=point_cloud_range, pretrained=dict(img="torchvision://resnet50"), img_backbone=dict( type="ResNet", depth=50, num_stages=4, out_indices=(3,), frozen_stages=1, norm_cfg=dict(type="BN", requires_grad=False), norm_eval=True, style="pytorch", ), img_neck=dict( type="FPN", in_channels=[2048], out_channels=_dim_, start_level=0, add_extra_convs="on_output", num_outs=_num_levels_, relu_before_extra_convs=True, ), # img_backbone=dict( # type="ResNet", # depth=101, # num_stages=4, # out_indices=(1, 2, 3), # frozen_stages=4, # norm_cfg=dict(type="BN2d", requires_grad=False), # norm_eval=True, # style="caffe", # dcn=dict( # type="DCNv2", deform_groups=1, fallback_on_stride=False # ), # original DCNv2 will print log when perform load_state_dict # stage_with_dcn=(False, False, True, True), # ), # img_neck=dict( # type="FPN", # in_channels=[512, 1024, 2048], # out_channels=_dim_, # start_level=0, # add_extra_convs="on_output", # num_outs=4, # relu_before_extra_convs=True, # ), freeze_img_backbone=True, freeze_img_neck=True, freeze_bn=True, freeze_bev_encoder=True, score_thresh=0.4, filter_score_thresh=0.35, qim_args=dict( qim_type="QIMBase", merger_dropout=0, update_query_pos=True, fp_ratio=0.3, random_drop=0.1, ), # hyper-param for query dropping mentioned in MOTR mem_args=dict( memory_bank_type="MemoryBank", memory_bank_score_thresh=0.0, memory_bank_len=4, ), loss_cfg=dict( type="ClipMatcher", num_classes=10, weight_dict=None, code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2], assigner=dict( type="HungarianAssigner3DTrack", cls_cost=dict(type="FocalLossCost", weight=2.0), reg_cost=dict(type="BBox3DL1Cost", weight=0.25), pc_range=point_cloud_range, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0 ), loss_bbox=dict(type="L1Loss", loss_weight=0.25), ), # loss cfg for tracking pts_bbox_head=dict( type="BEVFormerTrackHead", bev_h=bev_h_, bev_w=bev_w_, # num_query=900, num_query=100, num_classes=10, in_channels=_dim_, sync_cls_avg_factor=True, with_box_refine=True, as_two_stage=False, past_steps=past_steps, fut_steps=fut_steps, transformer=dict( type="PerceptionTransformer", rotate_prev_bev=True, use_shift=True, use_can_bus=True, embed_dims=_dim_, encoder=dict( type="BEVFormerEncoder", # num_layers=6, num_layers=1, pc_range=point_cloud_range, num_points_in_pillar=4, return_intermediate=False, transformerlayers=dict( type="BEVFormerLayer", attn_cfgs=[ dict( type="TemporalSelfAttention", embed_dims=_dim_, num_levels=1 ), dict( type="SpatialCrossAttention", pc_range=point_cloud_range, deformable_attention=dict( type="MSDeformableAttention3D", embed_dims=_dim_, num_points=8, num_levels=_num_levels_, ), embed_dims=_dim_, ), ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=( "self_attn", "norm", "cross_attn", "norm", "ffn", "norm", ), ), ), decoder=dict( type="DetectionTransformerDecoder", # num_layers=6, num_layers=1, return_intermediate=True, transformerlayers=dict( type="DetrTransformerDecoderLayer", attn_cfgs=[ dict( type="MultiheadAttention", embed_dims=_dim_, num_heads=8, dropout=0.1, ), dict( type="CustomMSDeformableAttention", embed_dims=_dim_, num_levels=1, ), ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=( "self_attn", "norm", "cross_attn", "norm"