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Getting started with MapTracker

In this document, we provide the commands for running inference/evaluation, training, and visualization.

Inference and evaluation

Inference and evaluate with Chamfer-based mAP

Run the following command to do inference and evaluation using the pretrained checkpoints, assuming 8 GPUs are used.

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7  bash tools/dist_test.sh  plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune.py    work_dirs/pretrained_ckpts/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune/latest.pth  8  --eval --eval-options save_semantic=True

Set the --eval-options save_semantic=True to also save the semantic segmentation results of the BEV module.

Evaluate with C-mAP

Generate prediction matching by

python tools/tracking/prepare_pred_tracks.py ${CONFIG} --result_path ${SUBMISSION_FILE} --cons_frames ${COMEBACK_FRAMES}

Evaluate with C-mAP by

python tools/tracking/calculate_cmap.py ${CONFIG} --result_path ${PRED_MATCHING_INFO}

An example evaluation:

python tools/tracking/calculate_cmap.py plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune.py --result_path ./work_dirs/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune/pos_predictions.pkl

Results

By running with the checkpoints we provided in the data preparation guide, the expected results are:

Dataset Split Divider Crossing Boundary mAP C-mAP
nuScenes old 74.14 80.04 74.06 76.08 69.13
nuScenes new 30.10 45.86 45.06 40.34 32.50
Argoverse2 old 76.99 79.97 73.66 76.87 68.35
Argoverse2 new 75.11 69.96 68.95 71.34 63.11

Training

The training consists of three stages as detailed in the paper. We train the models on 8 Nvidia RTX A5000 GPUs.

Stage 1: BEV pretraining with semantic segmentation losses:

bash ./tools/dist_train.sh plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage1_bev_pretrain.py 8

Stage 2: Vector module warmup with a large batch size while freezing the BEV module:

bash ./tools/dist_train.sh plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage2_warmup.py 8

Set up the load_from=... properly in the config file to load the checkpoint from stage 1.

Stage 3: Joint finetuning:

bash ./tools/dist_train.sh plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune.py 8

Set up the load_from=... properly in the config file to load the checkpoint from stage 2.

Visualization

Global merged reconstruction (merged from local HD maps)

python tools/visualization/vis_global.py [path to method configuration file under plugin/configs] \
  --data_path [path to the .pkl file] \
  --out_dir [path to the output folder] \
  --option [vis-pred / vis-gt: visualize predicted vectors / visualize ground truth vectors] \
  --per_frame_result 1

Set the --per_frame_result to 1 to generate the per-frame video, the visualization is a bit slow; set it to 0 to only produce the final merged global reconstruction.

Examples:

# Visualize MapTracker's prediction
python tools/visualization/vis_global.py plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune.py \
--data_path work_dirs/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune/pos_predictions.pkl \
--out_dir vis_global/nuscenes_old/maptracker \
--option vis-pred  --per_frame_result 1

# Visualize groud truth data
python tools/visualization/vis_global.py plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune.py \
--data_path datasets/nuscenes/nuscenes_map_infos_val_gt_tracks.pkl \
--out_dir vis_global/nuscenes_old/gt  \
--option vis-gt --per_frame_result 0

Local HD map reconstruction

python tools/visualization/vis_per_frame.py [path to method configuration file under plugin/configs] \
  --data_path [path to the .pkl file] \
  --out_dir [path to the data folder] \
  --option [vis-pred / vis-gt: visualize predicted vectors / visualize ground truth vectors and input video streams]

Note that the input perspective-view videos will be saved when generating the ground truth visualization.

Examples:

# Visualize MapTracker's prediction
python tools/visualization/vis_per_frame.py plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune.py \
--data_path work_dirs/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune/pos_predictions.pkl \
--out_dir vis_local/nuscenes_old/maptracker \
--option vis-pred

# Visualize groud truth data
python tools/visualization/vis_per_frame.py plugin/configs/maptracker/nuscenes_oldsplit/maptracker_nusc_oldsplit_5frame_span10_stage3_joint_finetune.py \
--data_path datasets/nuscenes/nuscenes_map_infos_val_gt_tracks.pkl \
--out_dir vis_local/nuscenes_old/gt  \
--option vis-gt